From 15a9a2ff1d4828b312a9e2fe4a3774af80aaf8b5 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 15:02:06 +0200
Subject: [PATCH 01/12] better assert

---
 src/shogun/mathematics/Statistics.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/shogun/mathematics/Statistics.cpp b/src/shogun/mathematics/Statistics.cpp
index db78e2448f7..52cf2b998a1 100644
--- a/src/shogun/mathematics/Statistics.cpp
+++ b/src/shogun/mathematics/Statistics.cpp
@@ -20,7 +20,7 @@ using namespace shogun;
 
 float64_t CStatistics::mean(SGVector<float64_t> values)
 {
-	ASSERT(values.vlen);
+	ASSERT(values.vlen>0);
 	ASSERT(values.vector);
 
 	float64_t sum=0;

From 4599e80c41597b02d88c81ae9e6eb5b1c03fb771 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 15:02:15 +0200
Subject: [PATCH 02/12] fixed compile error

---
 src/shogun/mathematics/Math.h | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/shogun/mathematics/Math.h b/src/shogun/mathematics/Math.h
index b4084c580e5..4e62b66ea6a 100644
--- a/src/shogun/mathematics/Math.h
+++ b/src/shogun/mathematics/Math.h
@@ -873,6 +873,7 @@ class CMath : public CSGObject
 		/** @deprecated use CStatistics::mean() instead */
 		static inline float64_t mean(float64_t* vec, int32_t len)
 		{
+			SG_SDEPRECATED;
 			ASSERT(vec);
 			ASSERT(len>0);
 

From 1eb0e2355284abce73ccd74b1d332e78b0e104ff Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:38:57 +0200
Subject: [PATCH 03/12] removed KernelPerceptron

---
 src/interfaces/modular/Classifier.i          |  2 -
 src/interfaces/modular/Classifier_includes.i |  1 -
 src/shogun/classifier/KernelPerceptron.cpp   | 60 ----------------
 src/shogun/classifier/KernelPerceptron.h     | 75 --------------------
 4 files changed, 138 deletions(-)
 delete mode 100644 src/shogun/classifier/KernelPerceptron.cpp
 delete mode 100644 src/shogun/classifier/KernelPerceptron.h

diff --git a/src/interfaces/modular/Classifier.i b/src/interfaces/modular/Classifier.i
index 89004d1bcb5..3755a827bc2 100644
--- a/src/interfaces/modular/Classifier.i
+++ b/src/interfaces/modular/Classifier.i
@@ -21,7 +21,6 @@
 %rename(GNPPSVM) CGNPPSVM;
 %rename(GPBTSVM) CGPBTSVM;
 %rename(GaussianNaiveBayes) CGaussianNaiveBayes;
-%rename(KernelPerceptron) CKernelPerceptron;
 %rename(KNN) CKNN;
 %rename(LDA) CLDA;
 %rename(LibLinear) CLibLinear;
@@ -79,7 +78,6 @@
 %include <shogun/classifier/svm/GMNPSVM.h>
 %include <shogun/classifier/svm/GNPPSVM.h>
 %include <shogun/classifier/svm/GPBTSVM.h>
-%include <shogun/classifier/KernelPerceptron.h> 
 %include <shogun/classifier/KNN.h>
 %include <shogun/classifier/LDA.h>
 %include <shogun/classifier/svm/LibLinear.h>
diff --git a/src/interfaces/modular/Classifier_includes.i b/src/interfaces/modular/Classifier_includes.i
index fde1033395f..c8700b49e8e 100644
--- a/src/interfaces/modular/Classifier_includes.i
+++ b/src/interfaces/modular/Classifier_includes.i
@@ -3,7 +3,6 @@
  #include <shogun/classifier/svm/GMNPSVM.h>
  #include <shogun/classifier/svm/GNPPSVM.h>
  #include <shogun/classifier/svm/GPBTSVM.h>
- #include <shogun/classifier/KernelPerceptron.h> 
  #include <shogun/machine/DistanceMachine.h>
  #include <shogun/classifier/KNN.h>
  #include <shogun/classifier/LDA.h>
diff --git a/src/shogun/classifier/KernelPerceptron.cpp b/src/shogun/classifier/KernelPerceptron.cpp
deleted file mode 100644
index ff2ab261c65..00000000000
--- a/src/shogun/classifier/KernelPerceptron.cpp
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation; either version 3 of the License, or
- * (at your option) any later version.
- *
- * Written (W) 1999-2009 Soeren Sonnenburg
- * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
- */
-
-#include <shogun/classifier/KernelPerceptron.h>
-#include <shogun/features/Labels.h>
-#include <shogun/mathematics/Math.h>
-
-using namespace shogun;
-
-CKernelPerceptron::CKernelPerceptron()
-: CKernelMachine()
-{
-}
-
-
-CKernelPerceptron::~CKernelPerceptron()
-{
-}
-
-bool CKernelPerceptron::train(CFeatures* data)
-{
-	ASSERT(labels);
-	//CLabels* train_labels=labels->get_int_labels(num_train_labels);
-
-//
-//# compute output activation y = f(w x)
-//# If y = t, don't change weights
-//# If y != t, update the weights:
-//
-//w(new) = w(old) + 2 m t x
-	return false;
-
-}
-
-bool CKernelPerceptron::load(FILE* srcfile)
-{
-	SG_SET_LOCALE_C;
-	SG_RESET_LOCALE;
-	return false;
-}
-
-bool CKernelPerceptron::save(FILE* dstfile)
-{
-	SG_SET_LOCALE_C;
-	SG_RESET_LOCALE;
-	return false;
-}
-
-
-float64_t CKernelPerceptron::classify_example(int32_t num)
-{
-	return 0;
-}
diff --git a/src/shogun/classifier/KernelPerceptron.h b/src/shogun/classifier/KernelPerceptron.h
deleted file mode 100644
index 355ff016a52..00000000000
--- a/src/shogun/classifier/KernelPerceptron.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation; either version 3 of the License, or
- * (at your option) any later version.
- *
- * Written (W) 1999-2009 Soeren Sonnenburg
- * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
- */
-
-#ifndef _KERNELPERCEPTRON_H___
-#define _KERNELPERCEPTRON_H___
-
-#include <stdio.h>
-#include <shogun/lib/common.h>
-#include <shogun/features/Features.h>
-#include <shogun/machine/KernelMachine.h>
-
-namespace shogun
-{
-/** @brief Class KernelPerceptron -
- * currently unfinished implementation of a Kernel Perceptron
- */
-class CKernelPerceptron : public CKernelMachine
-{
-	public:
-		/** constructor */
-		CKernelPerceptron();
-		virtual ~CKernelPerceptron();
-
-		/** train kernel perceptron classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
-		/** classify one specific example
-		 *
-		 * @param num which example to classify
-		 * @return classified value
-		 */
-		virtual float64_t classify_example(int32_t num);
-
-		/** load KernelPerceptron from file
-		 *
-		 * @param srcfile file to load from
-		 * @return if load was successful
-		 */
-		virtual bool load(FILE* srcfile);
-
-		/** save KernelPerceptron to file
-		 *
-		 * @param dstfile file to save to
-		 * @return if save was successful
-		 */
-		virtual bool save(FILE* dstfile);
-
-		/** get classifier type
-		 *
-		 * @return classifier type KERNELPERCEPTRON
-		 */
-		inline virtual EClassifierType get_classifier_type()
-		{
-			return CT_KERNELPERCEPTRON;
-		}
-
-		/** @return object name */
-		inline virtual const char* get_name() const { return "KernelPerceptron"; }
-};
-}
-#endif

From ebad67e9b3dfb23db8ce3cee29da9e7739458e27 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:40:15 +0200
Subject: [PATCH 04/12] added store_sv_features method to store SV data after
 training locally some changes go along with this train method now calls
 train_kernel_machine

---
 src/shogun/machine/KernelMachine.cpp | 27 +++++++++++++++++++++
 src/shogun/machine/KernelMachine.h   | 36 ++++++++++++++++++++++++++++
 2 files changed, 63 insertions(+)

diff --git a/src/shogun/machine/KernelMachine.cpp b/src/shogun/machine/KernelMachine.cpp
index a579288bd0b..15efb98325c 100644
--- a/src/shogun/machine/KernelMachine.cpp
+++ b/src/shogun/machine/KernelMachine.cpp
@@ -40,8 +40,11 @@ CKernelMachine::CKernelMachine()
 	m_parameters->add(&m_alpha, "m_alpha",
 			"Array of coefficients alpha.");
 	m_parameters->add(&m_svs, "m_svs", "Number of ``support vectors''.");
+	m_parameters->add(&m_store_sv_features, "store_sv_features",
+			"Should SV-feature be stored after training?");
 
 	m_bias=0.0;
+	m_store_sv_features=false;
 }
 
 CKernelMachine::~CKernelMachine()
@@ -265,3 +268,27 @@ void* CKernelMachine::apply_helper(void* p)
 
 	return NULL;
 }
+
+void CKernelMachine::store_sv_features()
+{
+	if (!kernel)
+		SG_ERROR("kernel is needed to store SV features.\n");
+
+	CFeatures* lhs=kernel->get_lhs();
+	CFeatures* rhs=kernel->get_rhs();
+
+	if (!lhs)
+		SG_ERROR("kernel lhs is needed to store SV features.\n");
+
+	/* copy sv feature data */
+	CFeatures* sv_features=lhs->copy_subset(m_svs);
+	SG_UNREF(lhs);
+
+	/* now sv indices are just the identity */
+	CMath::range_fill_vector(m_svs.vector, m_svs.vlen, 0);
+
+	/* set new lhs to kernel */
+	kernel->init(sv_features, rhs);
+
+	SG_UNREF(rhs);
+}
diff --git a/src/shogun/machine/KernelMachine.h b/src/shogun/machine/KernelMachine.h
index ce6a50761f3..a884a6c798a 100644
--- a/src/shogun/machine/KernelMachine.h
+++ b/src/shogun/machine/KernelMachine.h
@@ -57,6 +57,17 @@ class CKernelMachine : public CMachine
 		virtual const char* get_name(void) const {
 			return "KernelMachine"; }
 
+
+		virtual bool train(CFeatures* data=NULL)
+		{
+//			bool result=train_kernel_machine(data);
+
+			if (m_store_sv_features)
+				store_sv_features();
+
+			return false;
+		}
+
 		/** set kernel
 		 *
 		 * @param k kernel
@@ -332,6 +343,29 @@ class CKernelMachine : public CMachine
 		 */
 		static void* apply_helper(void* p);
 
+		/** TODO comment
+		 *
+		 */
+		inline void set_store_sv_features(bool store)
+		{
+			m_store_sv_features=store;
+		}
+
+	protected:
+		/** train classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * NOT IMPLEMENTED!
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL)=0;
+
+		virtual void store_sv_features();
+
 	protected:
 		/** kernel */
 		CKernel* kernel;
@@ -349,6 +383,8 @@ class CKernelMachine : public CMachine
 
 		/** array of ``support vectors'' (indices of feature objects) */
 		SGVector<int32_t> m_svs;
+
+		bool m_store_sv_features;
 };
 }
 #endif /* _KERNEL_MACHINE_H__ */

From 87d622b4ed714fd2a9b15e677909483dfbce1dcb Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:41:02 +0200
Subject: [PATCH 05/12] added method to copy a subset of features

---
 src/shogun/features/Features.h | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/src/shogun/features/Features.h b/src/shogun/features/Features.h
index 993c9ca5916..642d654cc25 100644
--- a/src/shogun/features/Features.h
+++ b/src/shogun/features/Features.h
@@ -171,7 +171,11 @@ class CFeatures : public CSGObject
 		 * @param num_vectors new number of vectors
 		 * @return if reshaping was successful
 		 */
-		virtual bool reshape(int32_t num_features, int32_t num_vectors) { return false; }
+		virtual bool reshape(int32_t num_features, int32_t num_vectors)
+		{
+			SG_NOTIMPLEMENTED;
+			return false;
+		}
 
 		/** get memory footprint of one feature
 		 *
@@ -262,6 +266,21 @@ class CFeatures : public CSGObject
 			return m_subset ? m_subset->subset_idx_conversion(idx) : idx;
 		}
 
+		/** Creates a new CFeatures instance containing copies of the elements
+		 * which are specified by the provided indices.
+		 *
+		 * This method is needed for a KernelMachine to store its model data.
+		 * NOT IMPLEMENTED!
+		 *
+		 * @param indices indices of feature elements to copy
+		 * @return new CFeatures instance with copies of feature data
+		 */
+		virtual CFeatures* copy_subset(SGVector<index_t> indices) const
+		{
+			SG_NOTIMPLEMENTED;
+			return NULL;
+		}
+
 	private:
 		/** feature properties */
 		uint64_t  properties;

From 7a629ffac127f9a3cdc5f7a45adea74a28707f00 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:47:02 +0200
Subject: [PATCH 06/12] removed debug stuff

---
 src/shogun/machine/KernelMachine.h | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/shogun/machine/KernelMachine.h b/src/shogun/machine/KernelMachine.h
index a884a6c798a..e96c3b06db7 100644
--- a/src/shogun/machine/KernelMachine.h
+++ b/src/shogun/machine/KernelMachine.h
@@ -60,12 +60,12 @@ class CKernelMachine : public CMachine
 
 		virtual bool train(CFeatures* data=NULL)
 		{
-//			bool result=train_kernel_machine(data);
+			bool result=train_kernel_machine(data);
 
 			if (m_store_sv_features)
 				store_sv_features();
 
-			return false;
+			return result;
 		}
 
 		/** set kernel
@@ -362,7 +362,11 @@ class CKernelMachine : public CMachine
 		 *
 		 * @return whether training was successful
 		 */
-		virtual bool train_kernel_machine(CFeatures* data=NULL)=0;
+		virtual bool train_kernel_machine(CFeatures* data=NULL)
+		{
+			SG_NOTIMPLEMENTED;
+			return false;
+		}
 
 		virtual void store_sv_features();
 

From bfa3fad6556bb3d70619c82df635e040f87c6139 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:48:20 +0200
Subject: [PATCH 07/12] removed KernelPerceptron

---
 src/shogun/ui/GUIClassifier.cpp | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/src/shogun/ui/GUIClassifier.cpp b/src/shogun/ui/GUIClassifier.cpp
index c58ecfa38bf..a24c8d60e1c 100644
--- a/src/shogun/ui/GUIClassifier.cpp
+++ b/src/shogun/ui/GUIClassifier.cpp
@@ -29,7 +29,6 @@
 #include <shogun/classifier/LPM.h>
 #include <shogun/classifier/LPBoost.h>
 #include <shogun/classifier/Perceptron.h>
-#include <shogun/classifier/KernelPerceptron.h>
 
 #include <shogun/machine/LinearMachine.h>
 
@@ -235,12 +234,6 @@ bool CGUIClassifier::new_classifier(char* name, int32_t d, int32_t from_d)
 		SG_INFO("created KRR object %p\n", classifier);
 	}
 #endif //HAVE_LAPACK
-	else if (strcmp(name,"KERNELPERCEPTRON")==0)
-	{
-		SG_UNREF(classifier);
-		classifier= new CKernelPerceptron();
-		SG_INFO("created Kernel Perceptron object\n") ;
-	}
 	else if (strcmp(name,"PERCEPTRON")==0)
 	{
 		SG_UNREF(classifier);
@@ -1114,7 +1107,6 @@ CLabels* CGUIClassifier::classify()
 		case CT_CPLEXSVM:
 		case CT_GMNPSVM:
 		case CT_GNPPSVM:
-		case CT_KERNELPERCEPTRON:
 		case CT_LIBSVR:
 		case CT_LIBSVMMULTICLASS:
 		case CT_LIBSVMONECLASS:
@@ -1209,7 +1201,6 @@ bool CGUIClassifier::get_trained_classifier(
 		case CT_GPBT:
 		case CT_CPLEXSVM:
 		case CT_GMNPSVM:
-		case CT_KERNELPERCEPTRON:
 		case CT_LIBSVR:
 		case CT_LIBSVMONECLASS:
 		case CT_SVRLIGHT:

From 0c66e74c3b557354c97ad70d520b7494738040cb Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 16:49:08 +0200
Subject: [PATCH 08/12] removed warning

---
 src/shogun/structure/SegmentLoss.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/shogun/structure/SegmentLoss.cpp b/src/shogun/structure/SegmentLoss.cpp
index 8da30a07f50..7e7636260e3 100644
--- a/src/shogun/structure/SegmentLoss.cpp
+++ b/src/shogun/structure/SegmentLoss.cpp
@@ -19,7 +19,7 @@ CSegmentLoss::CSegmentLoss()
 	m_segment_loss(1,1,2),
 	m_segment_ids(NULL),
 	m_segment_mask(NULL),
-	m_num_segment_types(NULL)
+	m_num_segment_types(0)
 {
 }
 CSegmentLoss::~CSegmentLoss()

From 545972e1e9e293165b24ed27c920c4e5c0694699 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 17:07:31 +0200
Subject: [PATCH 09/12] forbid subset on lhs when calling apply

---
 src/shogun/machine/KernelMachine.cpp | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/src/shogun/machine/KernelMachine.cpp b/src/shogun/machine/KernelMachine.cpp
index 15efb98325c..b7635b97df0 100644
--- a/src/shogun/machine/KernelMachine.cpp
+++ b/src/shogun/machine/KernelMachine.cpp
@@ -209,6 +209,15 @@ float64_t CKernelMachine::apply(int32_t num)
 {
 	ASSERT(kernel);
 
+	/* check that lhs does not have a subset. Might be very bad because of a
+	 * sv index kaboom: the sv indices are the ones for the original training
+	 * data, not for the subset, so better just forbid this */
+	CFeatures* lhs=kernel->get_lhs();
+	if (lhs && lhs->has_subset())
+		SG_ERROR("A subset on the lhs is not allowed when calling apply().\n");
+
+	SG_UNREF(lhs);
+
 	if (kernel->has_property(KP_LINADD) && (kernel->get_is_initialized()))
 	{
 		float64_t score = kernel->compute_optimized(num);

From 608d4641c74842a3ad8de4b9e0067db30ad6e265 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 17:07:45 +0200
Subject: [PATCH 10/12] added comments

---
 src/shogun/machine/KernelMachine.h | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/shogun/machine/KernelMachine.h b/src/shogun/machine/KernelMachine.h
index e96c3b06db7..c6270eef80b 100644
--- a/src/shogun/machine/KernelMachine.h
+++ b/src/shogun/machine/KernelMachine.h
@@ -62,6 +62,7 @@ class CKernelMachine : public CMachine
 		{
 			bool result=train_kernel_machine(data);
 
+			/* store sv feature data if wanted */
 			if (m_store_sv_features)
 				store_sv_features();
 
@@ -343,8 +344,9 @@ class CKernelMachine : public CMachine
 		 */
 		static void* apply_helper(void* p);
 
-		/** TODO comment
+		/** When set, store_sv_features() is called after training
 		 *
+		 * @param store whether sv features should be stored after training
 		 */
 		inline void set_store_sv_features(bool store)
 		{
@@ -368,6 +370,10 @@ class CKernelMachine : public CMachine
 			return false;
 		}
 
+		/** Stores feature data of the SV indices and sets it to the lhs of the
+		 * underlying kernel. Then, all SV indices are set to identity.
+		 * This way, this kernel machine now uses its own data copy for
+		 * classification. */
 		virtual void store_sv_features();
 
 	protected:
@@ -388,6 +394,7 @@ class CKernelMachine : public CMachine
 		/** array of ``support vectors'' (indices of feature objects) */
 		SGVector<int32_t> m_svs;
 
+		/** whether sv features should be stored after training */
 		bool m_store_sv_features;
 };
 }

From 4a2bcb739095e9f202f44bb81062b964e95961af Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 17:09:14 +0200
Subject: [PATCH 11/12] added has_subset method

---
 src/shogun/features/Features.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/shogun/features/Features.h b/src/shogun/features/Features.h
index 642d654cc25..46314ae9e1e 100644
--- a/src/shogun/features/Features.h
+++ b/src/shogun/features/Features.h
@@ -266,6 +266,8 @@ class CFeatures : public CSGObject
 			return m_subset ? m_subset->subset_idx_conversion(idx) : idx;
 		}
 
+		inline bool has_subset() const { return m_subset!=NULL; }
+
 		/** Creates a new CFeatures instance containing copies of the elements
 		 * which are specified by the provided indices.
 		 *

From 963da14fdd1aa6673f59559d7d0129e450993f39 Mon Sep 17 00:00:00 2001
From: Heiko Strathmann <heiko.strathmann@gmail.com>
Date: Thu, 21 Jul 2011 17:09:25 +0200
Subject: [PATCH 12/12] renamed train() to train_kernel_machine() which is
 private and being called from KernelMachine

---
 src/shogun/classifier/mkl/MKL.cpp             |  2 +-
 src/shogun/classifier/mkl/MKL.h               | 27 +++++++++----------
 src/shogun/classifier/mkl/MKLMultiClass.cpp   |  2 +-
 src/shogun/classifier/mkl/MKLMultiClass.h     | 19 +++++++------
 src/shogun/classifier/svm/CPLEXSVM.cpp        |  2 +-
 src/shogun/classifier/svm/CPLEXSVM.h          |  7 ++---
 .../classifier/svm/DomainAdaptationSVM.cpp    |  4 +--
 .../classifier/svm/DomainAdaptationSVM.h      | 20 +++++++-------
 src/shogun/classifier/svm/GMNPSVM.cpp         |  2 +-
 src/shogun/classifier/svm/GMNPSVM.h           | 21 ++++++++-------
 src/shogun/classifier/svm/GNPPSVM.cpp         |  2 +-
 src/shogun/classifier/svm/GNPPSVM.h           | 21 ++++++++-------
 src/shogun/classifier/svm/GPBTSVM.cpp         |  2 +-
 src/shogun/classifier/svm/GPBTSVM.h           | 21 ++++++++-------
 src/shogun/classifier/svm/LaRank.cpp          |  2 +-
 src/shogun/classifier/svm/LaRank.h            |  5 ++--
 src/shogun/classifier/svm/LibSVM.cpp          |  2 +-
 src/shogun/classifier/svm/LibSVM.h            | 21 ++++++++-------
 .../classifier/svm/LibSVMMultiClass.cpp       |  2 +-
 src/shogun/classifier/svm/LibSVMMultiClass.h  | 21 ++++++++-------
 src/shogun/classifier/svm/LibSVMOneClass.cpp  |  2 +-
 src/shogun/classifier/svm/LibSVMOneClass.h    | 21 ++++++++-------
 src/shogun/classifier/svm/MPDSVM.cpp          |  2 +-
 src/shogun/classifier/svm/MPDSVM.h            | 20 +++++++-------
 src/shogun/classifier/svm/SVMLight.cpp        |  2 +-
 src/shogun/classifier/svm/SVMLight.h          | 20 +++++++-------
 .../classifier/svm/SVMLightOneClass.cpp       |  2 +-
 src/shogun/classifier/svm/SVMLightOneClass.h  | 15 ++++++-----
 src/shogun/classifier/svm/ScatterSVM.cpp      |  2 +-
 src/shogun/classifier/svm/ScatterSVM.h        | 21 ++++++++-------
 src/shogun/regression/KRR.cpp                 |  6 ++---
 src/shogun/regression/KRR.h                   | 25 ++++++++---------
 src/shogun/regression/svr/LibSVR.cpp          |  2 +-
 src/shogun/regression/svr/LibSVR.h            | 21 ++++++++-------
 src/shogun/regression/svr/SVRLight.cpp        |  2 +-
 src/shogun/regression/svr/SVRLight.h          | 20 +++++++-------
 36 files changed, 198 insertions(+), 190 deletions(-)

diff --git a/src/shogun/classifier/mkl/MKL.cpp b/src/shogun/classifier/mkl/MKL.cpp
index b44f629de82..6a41328972d 100644
--- a/src/shogun/classifier/mkl/MKL.cpp
+++ b/src/shogun/classifier/mkl/MKL.cpp
@@ -190,7 +190,7 @@ bool CMKL::check_lpx_status(LPX *lp)
 }
 #endif // USE_GLPK
 
-bool CMKL::train(CFeatures* data)
+bool CMKL::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());
diff --git a/src/shogun/classifier/mkl/MKL.h b/src/shogun/classifier/mkl/MKL.h
index 44fd6e3677b..670e5dab350 100644
--- a/src/shogun/classifier/mkl/MKL.h
+++ b/src/shogun/classifier/mkl/MKL.h
@@ -133,17 +133,6 @@ class CMKL : public CSVM
 			return svm;
 		}
 
-
-		/** train MKL classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** set C mkl
 		 *
 		 * @param C new C_mkl
@@ -265,7 +254,20 @@ class CMKL : public CSVM
 		 */
 		virtual void compute_sum_beta(float64_t* sumw);
 
+		/** @return object name */
+		inline virtual const char* get_name() const { return "MKL"; }
+
 	protected:
+		/** train MKL classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 		/** check run before starting training (to e.g. check if labeling is
 		 * two-class labeling in classification case
 		 */
@@ -442,9 +444,6 @@ class CMKL : public CSVM
 		bool check_lpx_status(LPX *lp);
 #endif
 
-		/** @return object name */
-		inline virtual const char* get_name() const { return "MKL"; }
-
 	protected:
 		/** wrapper SVM */
 		CSVM* svm;
diff --git a/src/shogun/classifier/mkl/MKLMultiClass.cpp b/src/shogun/classifier/mkl/MKLMultiClass.cpp
index 408844bd1ee..06f0f237ab5 100644
--- a/src/shogun/classifier/mkl/MKLMultiClass.cpp
+++ b/src/shogun/classifier/mkl/MKLMultiClass.cpp
@@ -325,7 +325,7 @@ float64_t CMKLMultiClass::getsquarenormofprimalcoefficients(
 }
 
 
-bool CMKLMultiClass::train(CFeatures* data)
+bool CMKLMultiClass::train_kernel_machine(CFeatures* data)
 {
 	int numcl=labels->get_num_classes();
 	ASSERT(kernel);
diff --git a/src/shogun/classifier/mkl/MKLMultiClass.h b/src/shogun/classifier/mkl/MKLMultiClass.h
index 8c09bd58f80..4f4aec69d70 100644
--- a/src/shogun/classifier/mkl/MKLMultiClass.h
+++ b/src/shogun/classifier/mkl/MKLMultiClass.h
@@ -49,16 +49,6 @@ class CMKLMultiClass : public CMultiClassSVM
 	 */
 	virtual ~CMKLMultiClass();
 
-	/** train Multiclass MKL classifier
-	 *
-	 * @param data training data (parameter can be avoided if distance or
-	 * kernel-based classifiers are used and distance/kernels are
-	 * initialized with train data)
-	 *
-	 * @return whether training was successful
-	 */
-	virtual bool train(CFeatures* data=NULL);
-
 	/** get classifier type
 	 *
 	 * @return classifier type GMNPMKL
@@ -158,6 +148,15 @@ class CMKLMultiClass : public CMultiClassSVM
 			const int32_t ind);
 	
 
+	/** train Multiclass MKL classifier
+	 *
+	 * @param data training data (parameter can be avoided if distance or
+	 * kernel-based classifiers are used and distance/kernels are
+	 * initialized with train data)
+	 *
+	 * @return whether training was successful
+	 */
+	virtual bool train_kernel_machine(CFeatures* data=NULL);
 	
 
 protected:
diff --git a/src/shogun/classifier/svm/CPLEXSVM.cpp b/src/shogun/classifier/svm/CPLEXSVM.cpp
index 7fa60f2ef80..53913924be1 100644
--- a/src/shogun/classifier/svm/CPLEXSVM.cpp
+++ b/src/shogun/classifier/svm/CPLEXSVM.cpp
@@ -28,7 +28,7 @@ CCPLEXSVM::~CCPLEXSVM()
 {
 }
 
-bool CCPLEXSVM::train(CFeatures* data)
+bool CCPLEXSVM::train_kernel_machine(CFeatures* data)
 {
 	bool result = false;
 	CCplex cplex;
diff --git a/src/shogun/classifier/svm/CPLEXSVM.h b/src/shogun/classifier/svm/CPLEXSVM.h
index 130f221fe3a..c16d3f707ef 100644
--- a/src/shogun/classifier/svm/CPLEXSVM.h
+++ b/src/shogun/classifier/svm/CPLEXSVM.h
@@ -26,6 +26,9 @@ IGNORE_IN_CLASSLIST class CCPLEXSVM : public CSVM
 		CCPLEXSVM();
 		virtual ~CCPLEXSVM();
 
+		virtual inline EClassifierType get_classifier_type() { return CT_CPLEXSVM; }
+
+	protected:
 		/** train SVM classifier
 		 *
 		 * @param data training data (parameter can be avoided if distance or
@@ -34,9 +37,7 @@ IGNORE_IN_CLASSLIST class CCPLEXSVM : public CSVM
 		 *
 		 * @return whether training was successful
 		 */
-		virtual bool train(CFeatures* data=NULL);
-
-		virtual inline EClassifierType get_classifier_type() { return CT_CPLEXSVM; }
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
 };
 }
 #endif
diff --git a/src/shogun/classifier/svm/DomainAdaptationSVM.cpp b/src/shogun/classifier/svm/DomainAdaptationSVM.cpp
index 28b15776dea..973dbd6460d 100644
--- a/src/shogun/classifier/svm/DomainAdaptationSVM.cpp
+++ b/src/shogun/classifier/svm/DomainAdaptationSVM.cpp
@@ -78,7 +78,7 @@ bool CDomainAdaptationSVM::is_presvm_sane()
 }
 
 
-bool CDomainAdaptationSVM::train(CFeatures* data)
+bool CDomainAdaptationSVM::train_kernel_machine(CFeatures* data)
 {
 
 	if (data)
@@ -111,7 +111,7 @@ bool CDomainAdaptationSVM::train(CFeatures* data)
 	delete[] lin_term;
 
 	//train SVM
-	bool success = CSVMLight::train();
+	bool success = CSVMLight::train_kernel_machine();
 
 	ASSERT(presvm)
 
diff --git a/src/shogun/classifier/svm/DomainAdaptationSVM.h b/src/shogun/classifier/svm/DomainAdaptationSVM.h
index 69456f82c02..11a284ec50e 100644
--- a/src/shogun/classifier/svm/DomainAdaptationSVM.h
+++ b/src/shogun/classifier/svm/DomainAdaptationSVM.h
@@ -49,16 +49,6 @@ class CDomainAdaptationSVM : public CSVMLight
 		 * */
 		void init(CSVM* presvm, float64_t B);
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type
@@ -106,6 +96,16 @@ class CDomainAdaptationSVM : public CSVMLight
 		 */
 		virtual bool is_presvm_sane();
 
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	private:
 		void init();
 
diff --git a/src/shogun/classifier/svm/GMNPSVM.cpp b/src/shogun/classifier/svm/GMNPSVM.cpp
index 815269b6d01..4f78a44ed63 100644
--- a/src/shogun/classifier/svm/GMNPSVM.cpp
+++ b/src/shogun/classifier/svm/GMNPSVM.cpp
@@ -48,7 +48,7 @@ CGMNPSVM::init(void)
 	m_basealphas = NULL, m_basealphas_y = 0, m_basealphas_x = 0;
 }
 
-bool CGMNPSVM::train(CFeatures* data)
+bool CGMNPSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());
diff --git a/src/shogun/classifier/svm/GMNPSVM.h b/src/shogun/classifier/svm/GMNPSVM.h
index 79bc82df88c..fe57ce5f0ab 100644
--- a/src/shogun/classifier/svm/GMNPSVM.h
+++ b/src/shogun/classifier/svm/GMNPSVM.h
@@ -40,16 +40,6 @@ class CGMNPSVM : public CMultiClassSVM
 		/** default destructor */
 		virtual ~CGMNPSVM();
 
-		/** train SVM
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type GMNPSVM
@@ -70,6 +60,17 @@ class CGMNPSVM : public CMultiClassSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "GMNPSVM"; }
 
+	protected:
+		/** train SVM
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** required for CMKLMulticlass
 		 * stores the untransformed alphas of this algorithm
diff --git a/src/shogun/classifier/svm/GNPPSVM.cpp b/src/shogun/classifier/svm/GNPPSVM.cpp
index 3052510e058..8831b62bff9 100644
--- a/src/shogun/classifier/svm/GNPPSVM.cpp
+++ b/src/shogun/classifier/svm/GNPPSVM.cpp
@@ -29,7 +29,7 @@ CGNPPSVM::~CGNPPSVM()
 {
 }
 
-bool CGNPPSVM::train(CFeatures* data)
+bool CGNPPSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());
diff --git a/src/shogun/classifier/svm/GNPPSVM.h b/src/shogun/classifier/svm/GNPPSVM.h
index f5d1ffc6758..b3a8b673064 100644
--- a/src/shogun/classifier/svm/GNPPSVM.h
+++ b/src/shogun/classifier/svm/GNPPSVM.h
@@ -33,16 +33,6 @@ class CGNPPSVM : public CSVM
 
 		virtual ~CGNPPSVM();
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type GNPPSVM
@@ -51,6 +41,17 @@ class CGNPPSVM : public CSVM
 
 		/** @return object name */
 		inline virtual const char* get_name() const { return "GNPPSVM"; }
+
+	protected:
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
 };
 }
 #endif
diff --git a/src/shogun/classifier/svm/GPBTSVM.cpp b/src/shogun/classifier/svm/GPBTSVM.cpp
index 4bab0d7cc20..ceb47171905 100644
--- a/src/shogun/classifier/svm/GPBTSVM.cpp
+++ b/src/shogun/classifier/svm/GPBTSVM.cpp
@@ -30,7 +30,7 @@ CGPBTSVM::~CGPBTSVM()
 	SG_FREE(model);
 }
 
-bool CGPBTSVM::train(CFeatures* data)
+bool CGPBTSVM::train_kernel_machine(CFeatures* data)
 {
 	float64_t* solution;                     /* store the solution found       */
 	QPproblem prob;                          /* object containing the solvers  */
diff --git a/src/shogun/classifier/svm/GPBTSVM.h b/src/shogun/classifier/svm/GPBTSVM.h
index cc73348ceab..31928672008 100644
--- a/src/shogun/classifier/svm/GPBTSVM.h
+++ b/src/shogun/classifier/svm/GPBTSVM.h
@@ -34,16 +34,6 @@ class CGPBTSVM : public CSVM
 		CGPBTSVM(float64_t C, CKernel* k, CLabels* lab);
 		virtual ~CGPBTSVM();
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type GPBT
@@ -53,6 +43,17 @@ class CGPBTSVM : public CSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "GPBTSVM"; }
 
+	protected:
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** SVM model */
 		struct svm_model* model;
diff --git a/src/shogun/classifier/svm/LaRank.cpp b/src/shogun/classifier/svm/LaRank.cpp
index 20e8dc8938a..dd6b0c38a8e 100644
--- a/src/shogun/classifier/svm/LaRank.cpp
+++ b/src/shogun/classifier/svm/LaRank.cpp
@@ -602,7 +602,7 @@ CLaRank::~CLaRank ()
 	destroy();
 }
 
-bool CLaRank::train(CFeatures* data)
+bool CLaRank::train_kernel_machine(CFeatures* data)
 {
 	tau = 0.0001;
 
diff --git a/src/shogun/classifier/svm/LaRank.h b/src/shogun/classifier/svm/LaRank.h
index 3c9683d1b89..bf5a9b3d4f9 100644
--- a/src/shogun/classifier/svm/LaRank.h
+++ b/src/shogun/classifier/svm/LaRank.h
@@ -320,9 +320,6 @@ namespace shogun
 
 			virtual ~CLaRank ();
 
-			bool train(CFeatures* data);
-
-
 			// LEARNING FUNCTION: add new patterns and run optimization steps
 			// selected with adaptative schedule
 			virtual int32_t add (int32_t x_id, int32_t yi);
@@ -361,6 +358,8 @@ namespace shogun
 			void set_tau(float64_t t) { tau=t; };
 			float64_t get_tau() { return tau; };
 
+		protected:
+			bool train_kernel_machine(CFeatures* data);
 
 		private:
 			/*
diff --git a/src/shogun/classifier/svm/LibSVM.cpp b/src/shogun/classifier/svm/LibSVM.cpp
index 80e1ebbf792..f4b4053a6bf 100644
--- a/src/shogun/classifier/svm/LibSVM.cpp
+++ b/src/shogun/classifier/svm/LibSVM.cpp
@@ -29,7 +29,7 @@ CLibSVM::~CLibSVM()
 }
 
 
-bool CLibSVM::train(CFeatures* data)
+bool CLibSVM::train_kernel_machine(CFeatures* data)
 {
 	struct svm_node* x_space;
 
diff --git a/src/shogun/classifier/svm/LibSVM.h b/src/shogun/classifier/svm/LibSVM.h
index 9e7dafdbe12..95c149395b9 100644
--- a/src/shogun/classifier/svm/LibSVM.h
+++ b/src/shogun/classifier/svm/LibSVM.h
@@ -40,16 +40,6 @@ class CLibSVM : public CSVM
 
 		virtual ~CLibSVM();
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type LIBSVM
@@ -59,6 +49,17 @@ class CLibSVM : public CSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "LibSVM"; }
 
+	protected:
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** SVM problem */
 		svm_problem problem;
diff --git a/src/shogun/classifier/svm/LibSVMMultiClass.cpp b/src/shogun/classifier/svm/LibSVMMultiClass.cpp
index 7956165d05f..468c5945b50 100644
--- a/src/shogun/classifier/svm/LibSVMMultiClass.cpp
+++ b/src/shogun/classifier/svm/LibSVMMultiClass.cpp
@@ -28,7 +28,7 @@ CLibSVMMultiClass::~CLibSVMMultiClass()
 	//SG_PRINT("deleting LibSVM\n");
 }
 
-bool CLibSVMMultiClass::train(CFeatures* data)
+bool CLibSVMMultiClass::train_kernel_machine(CFeatures* data)
 {
 	struct svm_node* x_space;
 
diff --git a/src/shogun/classifier/svm/LibSVMMultiClass.h b/src/shogun/classifier/svm/LibSVMMultiClass.h
index 4aa0b4190e8..8f2e23beb39 100644
--- a/src/shogun/classifier/svm/LibSVMMultiClass.h
+++ b/src/shogun/classifier/svm/LibSVMMultiClass.h
@@ -34,16 +34,6 @@ class CLibSVMMultiClass : public CMultiClassSVM
 		CLibSVMMultiClass(float64_t C, CKernel* k, CLabels* lab);
 		virtual ~CLibSVMMultiClass();
 
-		/** train multiclass SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type LIBSVMMULTICLASS
@@ -53,6 +43,17 @@ class CLibSVMMultiClass : public CMultiClassSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "LibSVMMultiClass"; }
 
+	protected:
+		/** train multiclass SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** SVM problem */
 		svm_problem problem;
diff --git a/src/shogun/classifier/svm/LibSVMOneClass.cpp b/src/shogun/classifier/svm/LibSVMOneClass.cpp
index a1ddb25f700..2daa1eb02f5 100644
--- a/src/shogun/classifier/svm/LibSVMOneClass.cpp
+++ b/src/shogun/classifier/svm/LibSVMOneClass.cpp
@@ -29,7 +29,7 @@ CLibSVMOneClass::~CLibSVMOneClass()
 	SG_FREE(model);
 }
 
-bool CLibSVMOneClass::train(CFeatures* data)
+bool CLibSVMOneClass::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	if (data)
diff --git a/src/shogun/classifier/svm/LibSVMOneClass.h b/src/shogun/classifier/svm/LibSVMOneClass.h
index ce5265f16dc..7d24b40cdce 100644
--- a/src/shogun/classifier/svm/LibSVMOneClass.h
+++ b/src/shogun/classifier/svm/LibSVMOneClass.h
@@ -34,16 +34,6 @@ class CLibSVMOneClass : public CSVM
 		CLibSVMOneClass(float64_t C, CKernel* k);
 		virtual ~CLibSVMOneClass();
 
-		/** train SVM
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type LIBSVMONECLASS
@@ -53,6 +43,17 @@ class CLibSVMOneClass : public CSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "LibSVMOneClass"; }
 
+	protected:
+		/** train SVM
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** SVM problem */
 		svm_problem problem;
diff --git a/src/shogun/classifier/svm/MPDSVM.cpp b/src/shogun/classifier/svm/MPDSVM.cpp
index acf54493a82..9598dc3f419 100644
--- a/src/shogun/classifier/svm/MPDSVM.cpp
+++ b/src/shogun/classifier/svm/MPDSVM.cpp
@@ -29,7 +29,7 @@ CMPDSVM::~CMPDSVM()
 {
 }
 
-bool CMPDSVM::train(CFeatures* data)
+bool CMPDSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(labels);
 	ASSERT(kernel);
diff --git a/src/shogun/classifier/svm/MPDSVM.h b/src/shogun/classifier/svm/MPDSVM.h
index b2655902160..b00580ad345 100644
--- a/src/shogun/classifier/svm/MPDSVM.h
+++ b/src/shogun/classifier/svm/MPDSVM.h
@@ -32,16 +32,6 @@ class CMPDSVM : public CSVM
 		CMPDSVM(float64_t C, CKernel* k, CLabels* lab);
 		virtual ~CMPDSVM();
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type MPD
@@ -52,6 +42,16 @@ class CMPDSVM : public CSVM
 		inline virtual const char* get_name() const { return "MPDSVM"; }
 
 	protected:
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 		/** compute H
 		 *
 		 * @param i index of H
diff --git a/src/shogun/classifier/svm/SVMLight.cpp b/src/shogun/classifier/svm/SVMLight.cpp
index 5655f9954fb..f4a772a4a96 100644
--- a/src/shogun/classifier/svm/SVMLight.cpp
+++ b/src/shogun/classifier/svm/SVMLight.cpp
@@ -172,7 +172,7 @@ CSVMLight::~CSVMLight()
   delete[] primal;
 }
 
-bool CSVMLight::train(CFeatures* data)
+bool CSVMLight::train_kernel_machine(CFeatures* data)
 {
 	//certain setup params
 	mkl_converged=false;
diff --git a/src/shogun/classifier/svm/SVMLight.h b/src/shogun/classifier/svm/SVMLight.h
index f041a0acc31..11c0a280f25 100644
--- a/src/shogun/classifier/svm/SVMLight.h
+++ b/src/shogun/classifier/svm/SVMLight.h
@@ -246,16 +246,6 @@ class CSVMLight : public CSVM
   /** init SVM */
   void init();
 
-  /** train SVM classifier
-   *
-   * @param data training data (parameter can be avoided if distance or
-   * kernel-based classifiers are used and distance/kernels are
-   * initialized with train data)
-   *
-   * @return whether training was successful
-   */
-  virtual bool train(CFeatures* data=NULL);
-
   /** get classifier type
    *
    * @return classifier type LIGHT
@@ -654,6 +644,16 @@ class CSVMLight : public CSVM
 	float64_t *optimize_qp( QP *qp,float64_t *epsilon_crit, int32_t nx,
 			float64_t *threshold, int32_t& svm_maxqpsize);
 
+	/** train SVM classifier
+	 *
+	 * @param data training data (parameter can be avoided if distance or
+	 * kernel-based classifiers are used and distance/kernels are
+	 * initialized with train data)
+	 *
+	 * @return whether training was successful
+	 */
+	virtual bool train_kernel_machine(CFeatures* data=NULL);
+
  protected:
   /** model */
   MODEL* model;
diff --git a/src/shogun/classifier/svm/SVMLightOneClass.cpp b/src/shogun/classifier/svm/SVMLightOneClass.cpp
index 327fa4486de..d3aeda3cb08 100644
--- a/src/shogun/classifier/svm/SVMLightOneClass.cpp
+++ b/src/shogun/classifier/svm/SVMLightOneClass.cpp
@@ -48,7 +48,7 @@ CSVMLightOneClass::CSVMLightOneClass()
 {
 }
 
-bool CSVMLightOneClass::train(CFeatures* data)
+bool CSVMLightOneClass::train_kernel_machine(CFeatures* data)
 {
 	//certain setup params
 	mkl_converged=false;
diff --git a/src/shogun/classifier/svm/SVMLightOneClass.h b/src/shogun/classifier/svm/SVMLightOneClass.h
index 3f02c4d2645..5d9856f7992 100644
--- a/src/shogun/classifier/svm/SVMLightOneClass.h
+++ b/src/shogun/classifier/svm/SVMLightOneClass.h
@@ -41,6 +41,13 @@ class CSVMLightOneClass: public CSVMLight
 		/** default destructor */
 		virtual ~CSVMLightOneClass() { }
 
+		/** get classifier type
+		 *
+		 * @return classifier type LIGHTONECLASS
+		 */
+		virtual inline EClassifierType get_classifier_type() { return CT_LIGHTONECLASS; }
+
+	protected:
 		/** train one class svm
 		 *
 		 * @param data training data (parameter can be avoided if distance or
@@ -49,13 +56,7 @@ class CSVMLightOneClass: public CSVMLight
 		 *
 		 * @return whether training was successful
 		 */
-		virtual bool train(CFeatures* data=NULL);
-
-		/** get classifier type
-		 *
-		 * @return classifier type LIGHTONECLASS
-		 */
-		virtual inline EClassifierType get_classifier_type() { return CT_LIGHTONECLASS; }
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
 };
 }
 #endif //USE_SVMLIGHT
diff --git a/src/shogun/classifier/svm/ScatterSVM.cpp b/src/shogun/classifier/svm/ScatterSVM.cpp
index f97568b8558..d9656a62a3e 100644
--- a/src/shogun/classifier/svm/ScatterSVM.cpp
+++ b/src/shogun/classifier/svm/ScatterSVM.cpp
@@ -44,7 +44,7 @@ CScatterSVM::~CScatterSVM()
 	delete[] norm_wcw;
 }
 
-bool CScatterSVM::train(CFeatures* data)
+bool CScatterSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(labels && labels->get_num_labels());
 	m_num_classes = labels->get_num_classes();
diff --git a/src/shogun/classifier/svm/ScatterSVM.h b/src/shogun/classifier/svm/ScatterSVM.h
index 78ac3597fdb..61e1d9b1d06 100644
--- a/src/shogun/classifier/svm/ScatterSVM.h
+++ b/src/shogun/classifier/svm/ScatterSVM.h
@@ -70,16 +70,6 @@ class CScatterSVM : public CMultiClassSVM
 		/** default destructor */
 		virtual ~CScatterSVM();
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type LIBSVM
@@ -102,6 +92,17 @@ class CScatterSVM : public CMultiClassSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "ScatterSVM"; }
 
+	protected:
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	private:
 		void compute_norm_wc();
 		virtual bool train_no_bias_libsvm();
diff --git a/src/shogun/regression/KRR.cpp b/src/shogun/regression/KRR.cpp
index 583643626ba..46ef27bcd40 100644
--- a/src/shogun/regression/KRR.cpp
+++ b/src/shogun/regression/KRR.cpp
@@ -40,7 +40,7 @@ CKRR::~CKRR()
 	delete[] alpha;
 }
 
-bool CKRR::train(CFeatures* data)
+bool CKRR::train_kernel_machine(CFeatures* data)
 {
 	delete[] alpha;
 
@@ -96,7 +96,7 @@ bool CKRR::save(FILE* dstfile)
 	return false;
 }
 
-CLabels* CKRR::classify()
+CLabels* CKRR::apply()
 {
 	ASSERT(kernel);
 
@@ -127,7 +127,7 @@ CLabels* CKRR::classify()
 	return output;
 }
 
-float64_t CKRR::classify_example(int32_t num)
+float64_t CKRR::apply(int32_t num)
 {
 	ASSERT(kernel);
 
diff --git a/src/shogun/regression/KRR.h b/src/shogun/regression/KRR.h
index d341befb11b..5835fa698a2 100644
--- a/src/shogun/regression/KRR.h
+++ b/src/shogun/regression/KRR.h
@@ -72,28 +72,18 @@ class CKRR : public CKernelMachine
 		 */
 		inline void set_tau(float64_t t) { tau = t; };
 
-		/** train regression
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based regressors are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** classify regression
 		 *
 		 * @return resulting labels
 		 */
-		virtual CLabels* classify();
+		virtual CLabels* apply();
 
 		/** classify one example
 		 *
 		 * @param num which example to classify
 		 * @return result
 		 */
-		virtual float64_t classify_example(int32_t num);
+		virtual float64_t apply(int32_t num);
 
 		/** load regression from file
 		 *
@@ -121,6 +111,17 @@ class CKRR : public CKernelMachine
 		/** @return object name */
 		inline virtual const char* get_name() const { return "KRR"; }
 
+	protected:
+		/** train regression
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based regressors are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	private:
 		/** alpha */
 		float64_t *alpha;
diff --git a/src/shogun/regression/svr/LibSVR.cpp b/src/shogun/regression/svr/LibSVR.cpp
index baa6cd3f9f4..39d7e2c1803 100644
--- a/src/shogun/regression/svr/LibSVR.cpp
+++ b/src/shogun/regression/svr/LibSVR.cpp
@@ -35,7 +35,7 @@ CLibSVR::~CLibSVR()
 	SG_FREE(model);
 }
 
-bool CLibSVR::train(CFeatures* data)
+bool CLibSVR::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());
diff --git a/src/shogun/regression/svr/LibSVR.h b/src/shogun/regression/svr/LibSVR.h
index 10caca99214..13d91308a20 100644
--- a/src/shogun/regression/svr/LibSVR.h
+++ b/src/shogun/regression/svr/LibSVR.h
@@ -64,16 +64,6 @@ class CLibSVR : public CSVM
 		CLibSVR(float64_t C, float64_t epsilon, CKernel* k, CLabels* lab);
 		virtual ~CLibSVR();
 
-		/** train regression
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based regressor are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifie type LIBSVR
@@ -82,6 +72,17 @@ class CLibSVR : public CSVM
 
 		/** @return object name */
 		inline virtual const char* get_name() const { return "LibSVR"; }
+
+	protected:
+		/** train regression
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based regressor are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
 	protected:
 		/** SVM problem */
 		svm_problem problem;
diff --git a/src/shogun/regression/svr/SVRLight.cpp b/src/shogun/regression/svr/SVRLight.cpp
index 7c1ed1f51b9..f885fe6eb1e 100644
--- a/src/shogun/regression/svr/SVRLight.cpp
+++ b/src/shogun/regression/svr/SVRLight.cpp
@@ -59,7 +59,7 @@ CSVRLight::CSVRLight()
 {
 }
 
-bool CSVRLight::train(CFeatures* data)
+bool CSVRLight::train_kernel_machine(CFeatures* data)
 {
 	//certain setup params
 	verbosity=1;
diff --git a/src/shogun/regression/svr/SVRLight.h b/src/shogun/regression/svr/SVRLight.h
index 67980949c96..1ea88193a5f 100644
--- a/src/shogun/regression/svr/SVRLight.h
+++ b/src/shogun/regression/svr/SVRLight.h
@@ -77,16 +77,6 @@ class CSVRLight: public CSVMLight
 		/** default destructor */
 		virtual ~CSVRLight() { }
 
-		/** train regression
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based regressors are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type SVRLIGHT
@@ -240,6 +230,16 @@ class CSVRLight: public CSVMLight
 			return kernel->kernel(i, j);
 		}
 
+		/** train regression
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based regressors are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 		/** number of train elements */
 		int32_t num_vectors;
 };