Description:

We provide a general solver for squared hinge loss SVM of the form:

min_{w,b} sum_i max(0,y_i(x_i^Tw+b))^2 + Omega(w)

where Omega(w) can be :

• l1 : Omega(w)=sum_i |w_i|
• l2 : Omega(w)=sum_i |w_i|^2
• l1-l2: Omega(w)=sum_g ||w_g||_2
• l1-lp: Omega(w)=sum_g ||w_g||_p
• adaptive l1-l2: Omega(w)=sum_g beta_g||w_g||_2

We also provide a multitask solver where T tasks can be learned simultaneously with joint sparsity constraints (mixed norm regularization).

Note that this toolbox has been designed to be efficient for dense data whereas most of the existing linear svm solvers have been designed for sparse datasets.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Any Format Supported By Matlab

Tags: Large Scale, Kernelmachine, Svm, Bci, Classification, Support Vector Machines, Feature Selection, Linear Svm, Convex Optimization, Gradient Based Learning, Manifold Learning, Optimization, Algorithms, Feature Weighting, Trace Norm, Toolbox, Group Lasso, Lasso, Sparse Learning, Quadratic Programming, Weighting, L1 Regularization, Large Datasets, Regularization, Pattern Recognition, Discriminant Analysis, Linear Model, Generalized Linear Models, Multiclass Support Vector Machine, L1 Minimization, Sparse Representation, L1 Norm, L21 Norm, Dimension Reduction, Multi Task

Archive: download here

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