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.
- 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 , the critical sections are C-extensions written in Cython . It makes heavy use of Numpy , a numeric computing library for Python.
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).
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
For detailed documentation see http://pprett.github.com/bolt/.
[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.