Demo displaying the performance of SVR in Shogun. You can draw your own points or have some generated for you from the "Toy Data" panel.
You can also play around with the various parameters to see how they effect the outcome.
Demonstration of performing regression with Gaussian Processes in Shogun. A detailed how-to can be found here.
You can enter your own data points by clicking on the canvas below or you can have some generated for you from the "Toy Data" panel on the right.
You can also experiment with the different arguments to see how they affect the outcome.
Demonstration of multiclass classification with Shogun, using the GMNPSVM class.
You can select a class from the appropriate area below the canvas and then draw points for that class by clicking on the canvas or have some data generated for you from the 'Toy Data' panel on the right. You can also experiment with the various parameters on the right to see how they affect the outcome.
Demonstration of a binary classification task with Shogun, using the CLibSVM class.
You can enter instances of the red and blue classes by left and right-clicking on the canvas below or you can have some points generated for you from the 'Toy Data' panel on the right.You can also experiment with the various parameters on the right to see how they affect the outcome.
Demonstration of binary classification with Shogun, using the CPerceptron class which will provide us with a linear classifier.
You can enter instances of the red and blue classes by left and right-clicking on the canvas below.
You can also experiment with the various parameters on the right to see how they affect the outcome.
This application demo uses a previously trained MulticlassLibLinear svm, in conjuction with the HashedDocDotFeatures, to predict the language of documents.
It works for 5 languages: English, Greek, German, Italian and Spanish.
This demo application uses a previously trained CGMNPSVM svm in combination with the Gaussian Kernel to recognize hand-written digits.
To test it, draw a digit (0..9) in the area below and press recognize!
Clustering demonstration using the CKMeans class of Shogun. More information on the k-means clustering algorithm can be found here.
You can enter data points by clicking on the canvas below or you can have some generated for you from the "Toy Data" panel on the right.
You can also experiment with the arguments to see how they affect the outcome.
Click on the canvas below to enter some points and experiment with the various arguments to see how the kernel matrix is affected each time.
You can also have some data points generated for you from the "Toy Data" panel on the right
Two dimensional visualization of different datasets by using various dimensionality reduction algorithms available in Shogun through Tapkee.
'words_embedding' uses the CKernelLocallyLinearEmbedding class.
'promoters_embedding' uses the CMultidimensionalScaling class.
All the rest use the CLocallyLinearEmbedding class.

What's New

Feb. 17, 2014 -> SHOGUN 3.2.0
Jan. 6, 2014 -> SHOGUN 3.1.1
Jan. 5, 2014 -> SHOGUN 3.1.0
Oct. 28, 2013 -> SHOGUN 3.0.0
March 17, 2013 -> SHOGUN 2.1.0
Sept. 1, 2012 -> SHOGUN 2.0.0
Dec. 1, 2011 -> SHOGUN 1.1.0