add fig & example of regression

Xtra-Computing · Nov 2, 2017 · 59e6d4c · 59e6d4c
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diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # ThunderSVM
 ## Documentations
-The documentations are available [here](https://mascot.readthedocs.io/en/thundersvm/) in Readthedoc.
+The documentations are available [here](https://thundersvm.readthedocs.io) in Readthedoc.
 
 ## TODO
 - integrate with interfaces

diff --git a/docs/_static/overall.png b/docs/_static/overall.png
diff --git a/docs/get-started.md b/docs/get-started.md
@@ -25,8 +25,11 @@ make -j runtest
 ```
 Make sure all the test cases pass.
 
-## Training SVMs for MNIST
-ThunderSVM uses the same command line options as LibSVM, so existing users of LibSVM can use ThunderSVM quickly. For new users of SVMs, the [user guide](http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf) provided in the LibSVM website also helps. In the following, we provide an example of using ThunderSVM for the MNIST dataset.
+## Training SVMs
+We show some concrete examples of using ThunderSVM. ThunderSVM uses the same command line options as LibSVM, so existing users of LibSVM can use ThunderSVM quickly. For new users of SVMs, the [user guide](http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf) provided in the LibSVM website also helps. 
+
+### Training SVMs for Classification
+In the following, we provide an example of using ThunderSVM for the MNIST dataset.
 
 * Download the MNIST data set
 The data set is available in [this link](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.scale.bz2).
@@ -45,4 +48,20 @@ Instructions available in [How To](how-to.md) page.
 ```
 The meaning of each option can be found in the [parameters](parameters.md) page. Then you will see ThunderSVM automatically choose multi-class SVMs as the training algorithm. The training takes a while to complete. Once completed, you can see the training error is xx%.
 
-The usage of other SVM algorithms (such as SVM regression) are similar to the above example. The key different is the selection of the options.
+### Training SVMs for Regression
+The usage of other SVM algorithms (such as SVM regression) are similar to the above example. The key different is the selection of the options. Let's take Abalone data set as an example.
+
+* Download the Abalone data set
+The data set is available in [this link](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/abalone_scale). You can use the following command to download.
+```bash
+wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/abalone_scale
+```
+
+* Install ThunderSVM
+Instructions available in [How To](how-to.md) page.
+
+* Run ThunderSVM
+```bash
+./thundersvm -s 3 -t 2 -g 3.8 -c 1000 abalone_scale svm.model
+```
+The meaning of each option can be found in the [parameters](parameters.md) page. 
diff --git a/docs/index.md b/docs/index.md
@@ -5,9 +5,11 @@ The mission of ThunderSVM is to help users easily and efficiently apply SVMs to
 * Support one-class, binary and multi-class SVM classification, SVM regression, and SVMs with probability outputs.
 * Have Python, R and Matlab interfaces.
 
+<center><img src="_static/overall.png" alt="performance" style="width: 400px;"></center>
+
 ## More information about ThunderSVM
 * [Getting Started](get-started.md)
 * [Introduction](intro.md)
 * [Parameters](parameters.md)
 * [How To](how-to.md)
-* [FAQ](faq.md)
+* [FAQ](faq.md)