The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from large-scale data.
The computational effort of OCAS scales linearly with the sample size. In an extensive empirical evaluation OCAS significantly outperforms current state of the art SVM solvers, like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise Support Vector solution. OCAS even in the early optimization steps shows often faster convergence than the so far in this domain prevailing approximative methods SGD and Pegasos. Effectively parallelizing OCAS we were able to train on a dataset of size 15 million examples (itself about 32GB in size) in just 671 seconds --- a competing string kernel SVM required 97,484 seconds to train on 10 million examples sub-sampled from this dataset.
New: The library also implements COFFIN framework for efficient training of translation invariant image classifiers from virtual examples. As an example, we used the implemented framework to train a linear SVM on a gender classification dataset of almost 5 million images on a plain notebook with just 4GB of memory.
- SVM solvers for training linear classifiers from large scale-data
- Binary (two-class) and genuine multi-class SVM formulations.
- Optimized code written in C.
- A stand alone application and MEX interface for Matlab.
- Reads examples from SVM^light format.
- Optimized for both sparse and dense features.
- Parallelized version of the binary solver.
- Tools for classification.
- Training translation invariant image classifiers from virtual examples.
- Functions for computing image features based on Local Binary Patterns (LBP)
- Changes to previous version:
Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.
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