【分享】SLR Toolbox for Classification Problems(用于分类问题的SLR工具包)

Data Intro

History

2009 Aug.10th: Version 1.2.1alpha

Minor bugs fixed.

2009 July 28th : Version 1.2 alpha

Two new binary calssfication algortihms (L1-norm based SLR) are implmented (but has not been tested carefully).

2009 June 5th Version 1.1alpha

A new multiclass classifier (SLR based one-versus-one classifier) is added.

2009 June 3rd : Version 1.0beta

Features

Sparse parameter estimation

Selection of relevant features during estimating weight parameters of a classifier

Appropriate for classification problems with high dimensional features

Avoid overfitting to some extent

Basically no need to tune parameters in algorithms

Covered Algorithms

Four types of binary classifiers (seven algorithms)

SLR (sparse, linear boundary)

L2-regularized LR (non-sparse, linear boundary)

Relevance vector machine (non-sparse in feature space, linear or non-linear boundary)

L1-regularized LR (sparse, linear boundary)

Four types of multi-class classifiers (six algorithms)

Sparse multinomial LR (sparse, linear boundary)

L2-regularized LR (non-sparse, linear boundary)

SLR one-versus-the-rest (sparse, linear boundary)

SLR one-versus-one (sparse, linear boundary)

Environment

    The codes in the toolbox were written for MATLAB ver7.0.1 or later under UNIX.

Several functions require the optimization toolbox.

 

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