LIBOL / LIBOL

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LIBOL - A Library for Online Learning Algorithms
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                    V0.3.0
                    
Available at http://libol.stevenhoi.org

Authors: Steven C.H. Hoi, Jialei Wang, Peilin Zhao

	[email protected]

	Nanyang Technological University
	School of Computer Engineering	
	Singapore 639798	

Scientific results produced using the software provided shall acknowledge
the use of LIBOL. Please cite as

    Steven C.H. Hoi, Jialei Wang, and Peilin Zhao.
    LIBOL: A Library for Online Learning Algorithms.
    Nanyang Technological University, 2012.
    Software available at http://libol.stevenhoi.org/

Moreover shall the author of LIBOL be informed about the publications.

About LIBOL
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LIBOL is a simple package for solving large-scale online learning tasks. 
This version has a total of 16 different linear online algorithms for binary classification, 
and a total of 13 different algorithms and variants for multiclass classification. 

Specifically, LIBOL consists of a family of first order online learning algorithms as follows
- Perceptron: the classical online learning algorithm (Rosenblatt, 1958);
- ALMA: A New ApproximateMaximal Margin Classification Algorithm Gentile (2001);
- ROMMA: the relaxed online maxiumu margin algorithms (Li and Long, 2002);
- OGD: the Online Gradient Descent (OGD) algorithms (Zinkevich, 2003);
- PA: Passive Aggressive (PA) algorithms (Crammer et al., 2006);
and a family of second order online learning algorithms as follows
- SOP: the Second Order Perceptron (SOP) algorithm (Cesa-Bianchi et al., 2005);
- CW: the Confidence-Weighted (CW) learning (Dredze et al., 2008);
- IELLIP: online learning algorithms by improved ellipsoid method Yang et al. (2009);
- AROW: the Adaptive Regularization of Weight Vectors (Crammer et al., 2009);
- NAROW: New variant of Adaptive Regularization (Orabona and Crammer, 2010);
- NHERD: the Normal Herding method via Gaussian Herding (Crammer and Lee, 2010)
- SCW: the recently proposed Soft ConfidenceWeighted algorithms (Wang et al., 2012).

This document briefly explains the usage of LIBOL. A more detailed manual can be found from the LIBOL web site.

To get started, please read the ``Quick Start'' section first.
For developers, please check the "run_experiment.m" to learn how to explore LIBOL for developing your new algorithms.

Table of Contents
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- Quick Start
- Installation
- Examples
- Additional Information

Quick Start
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See the section ``Installation'' for installing LIBOL.

Type "make", and then "demo" to get started 

Installation
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Simply type `make' to compile a list of core C++ programs (including "libsvmread"), and to add the paths of core algorithms ("algorithms", "algorithms_mex", "libsvm", "weka2matlab") automatically.

If you do not wish to re-compile the library, you can simply type 'init' to initialize the library by adding the necessary paths. 

Examples
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>> demo

Apply the online "Perceptron" algorithm on the default data set "svmguide3"

Note: By default, one random trial is applied for automated parameter selection and a total of 20 runs will be applied to obtain average results; The rest is the same.

>> demo('bc')

Demo of binary classification. Apply the online "Perceptron" algorithm on the default data set "svmguide3"

>> demo('mc')

Demo of multiclass classification. Apply the online multi-class Perceptron ("PerceptronM") algorithm on the default data set "glass"

>> demo('bc','PA','svmguide3')

Apply the online "PA" algorithm on the matlab-format binary-class data set "svmguide3"

>> demo('bc','PA','w1a','libsvm','c')

Apply the online "PA" algorithm on the libsvm-format binary-class data set "w1a" (where the core function is based on the 'C' implementation).

>> demo('bc','Perceptron','sonar','arff')

Apply the "Perceptron" algorithm on the weka-ARFF-format data set "sonar.arff"  % This requires to install WEKA and add WEKA.jar path to the matlab Java class path

>> demo('mc','M_PA','glass')

Apply the online multiclass "PA" (M_PA) algorithm on the matlab-format multiclass data set "glass" (where the core function is by default using 'matlab' implementation).

>> run_experiment('bc')

Run the entire set of binary classification experiments for side-by-side comparisons of all algorithms on the default data set "svmguide3" (by default using matlab implementation)

>> run_experiment('mc')

Run the entire set of multiclass classification experiments for side-by-side comparisons of all algorithms on the default data set "glass" (by default using matlab implementation)

>> run_experiment('bc','svmguide3','mat','c')

Run the entire set of binary classification experiments for side-by-side comparisons of all algorithms on the matlab-format data set "svmguide3" using C implementation

>> run_experiment('mc','glass','mat','c')

Run the entire set of multi-class experiments for side-by-side comparisons of all algorithms on the matlab-format data set "glass" using C implementation

Additional Information
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If you have any questions and comments, please send your email to [email protected]

For more info about LIBOL, please visit http://libol.stevenhoi.org/

Last updated date: 12 December, 2013.