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Bolt Online Learning Toolbox
C Python
tree: 51a2cb2513

cosmit

latest commit 51a2cb2513
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README.rst

Bolt Online Learning Toolbox

Bolt features discriminative learning of linear predictors (e.g. SVM or Logistic Regression) using fast online learning algorithms. Bolt is aimed at large-scale, high-dimensional and sparse machine-learning problems. In particular, problems encountered in information retrieval and natural language processing.

Bolt features:

  • Fast learning based on stochastic gradient descent (plain and via projected (sub-)gradients).
  • Different loss functions for classification (hinge, log, modified huber) and regression (OLS, huber).
  • Different penalties (L2, L1, and elastic-net).
  • Simple, yet powerful commandline interface similar to SVM^light.
  • Python bindings, feature vectors encoded as Numpy arrays.

Furthermore, Bolt provides support for generalized linear models for multi-class classification. Currently, it supports the following multi-class learning algorithms:

  • One-versus-All strategy for binary classifiers.
  • Multinomial Logistic Regression (aka MaxEnt) via SGD.
  • Averaged Perceptron [Freund, Y. and Schapire, R. E., 1998].

The toolkit is written in Python [1], the critical sections are C-extensions written in Cython [2]. It makes heavy use of Numpy [3], a numeric computing library for Python.

Requirements

To install Bolt you need:

  • Python 2.5 or 2.6
  • C-compiler (tested with gcc 4.3.3)
  • Numpy (>= 1.1)

If you want to modify *.pyx files you also need cython (>=0.11.2).

Installation

To clone the repository run,

git clone git://github.com/pprett/bolt.git

To build bolt simply run,

python setup.py build

To install bolt on your system, use

python setup.py install

Documentation

For detailed documentation see http://pprett.github.com/bolt/.

References

[1] http://www.python.org

[2] http://www.cython.org

[3] http://numpy.scipy.org

[Freund, Y. and Schapire, R. E., 1998] Large margin classification using the perceptron algorithm. In Machine Learning, 37, 277-296.

[Shwartz, S. S., Singer, Y., and Srebro, N., 2007] Pegasos: Primal estimated sub-gradient solver for svm. In Proceedings of ICML '07.

[Tsuruoka, Y., Tsujii, J., and Ananiadou, S., 2009] Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty. In Proceedings of the AFNLP/ACL '09.

[Zhang, T., 2004] Solving large scale linear prediction problems using stochastic gradient descent algorithms. In Proceedings of ICML '04.

[Zou, H., and Hastie, T., 2005] Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B, 67 (2), 301-320.

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