Description:

The Chestnut Machine Learning Library is a suite of machine learning algorithms written in Python with some code written in C for efficiency. Most algorithms are called with a simple, functional API with input data encoded as arrays. Some packages have a MATLAB-like API to enable migration from a MATLAB environment. The class hierarchy is minimal to enable new users to quickly learn and call Chestnut code from their own codes. The first alpha release includes the following packages:

• boosting: AdaBoost, LPBoost, BrownBoost, MADABoost, SoftBoost, TotalBoost (some boosters require CVXOPT)
• hmm: hidden markov models with support for discrete and continuous emission distributions and multiple training sequences.
• cluster: hierarchical, k-means, QT, and shifting means clustering algorithms
• knn: Voronoi tesselations, kd-trees, k-nearest neighbor classifiers
• linear: basic Fisher's linear discriminant analysis

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Nips2008

Archive: download here

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