DOGMA

DOGMA: a MATLAB toolbox for online learning. A ”dogma” is ”the established belief or doctrine held by a religion, ideology or any kind of organization: it is authoritative and not to be disputed, doubted or diverged from” (from Wikipedia). In antithesis with its name, DOGMA aims to unveil the ”mistery” that surrounds online kernel algorithms, showing how it is easy to design and use them. Are they really ”good” algorithms? Which one is better for a particular applications? How do they work? You can reply to all these questions just trying them, playing with them, modifying their codes, with DOGMA! DOGMA is a MATLAB toolbox for discriminative online learning. It implements all the state of the art algorithms in a unique framework. The main aim of the library is simplicity: all the implemented algorithms are easy to be used, understood, and modified. For this reason, all the implementations are in plain MATLAB, limiting the use of mex files only when it is strictly necessary. The library focuses on linear and kernel online algorithms, mainly developped in the ”relative mistake bound” framework. Examples are Perceptron, Passive-Aggresive, ALMA, NORMA, SILK, Projectron, RBP, Banditron, etc. The toolbox will be constantly updated as soon as new online algorithms are published in the scientific literature. Submissions from external authors are also encouraged.

References in zbMATH (referenced in 1 article )

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  1. Orabona, Francesco; Castellini, Claudio; Caputo, Barbara; Jie, Luo; Sandini, Giulio: On-line independent support vector machines (2010)