From e770dc56e4f99bd3b4d6e0f1f28f87c10813749a Mon Sep 17 00:00:00 2001
From: Abhijeet <abhijeetkislay@gmail.com>
Date: Mon, 24 Mar 2014 09:02:59 +0530
Subject: [PATCH] abstract revised for SupportVectorMachines.ipynb

---
 .../classification/SupportVectorMachines.ipynb              | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
index 645ecb2a841..f03d5169fc1 100644
--- a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
+++ b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:f55c143e085396584ccb5ea4ac209c4d7b9d4ad0d473b0e787f886479ac93fa8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -27,8 +28,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "This notebook illustrates how to train a binary support vector machine (SVM) classifier with shogun. \n",
-      "A classifier attempts to distinguish objects of different type. In case of of binary classification there are just two types of objects that we want to distinguish."
+      "This notebook illustrates how to train a binary <a href=\"http://en.wikipedia.org/wiki/Support_vector_machine\">support vector machines</a> (SVM) <a href=\"http://en.wikipedia.org/wiki/Statistical_classification\">classifier</a> using shogun. Here the training and testing data is generated by sampling from a <a href=\"http://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model\">gaussian mixture model</a> (GMM). <a href=\"http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CLibSVM.html\">CLibSVM</a> class of shogun is used to train with this <a href=\"http://en.wikipedia.org/wiki/Sampled_Gaussian_kernel#The_sampled_Gaussian_kernel\">sampled Gaussian Kernel</a>. "
      ]
     },
     {