K-tree provides a scalable approach to clustering by combining the B+-tree and k-means algorithms. Clustering can be used to solve problems in signal processing, machine learning, and other contexts. It has recently been used to solve document clustering problems on the Wikipedia collection.
x2search is a crawler based on machine learning algorithms that finds pages and documents that are similar to given positive and different to given negative examples. The learned classifiers can be exported and saved for later reuse. It features multiple settings for searching by domain/server, etc. and has a plug-in mechanism for adding document types to be searched.
scikits.learn is a Python module that integrates classic machine learning algorithms in the tightly-knit world of scientific Python packages. It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.
MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. It addresses the two most common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars), and item prediction from implicit feedback (e.g. from clicks or purchase actions). It contains dozens of recommender engines, including state-of-the-art matrix factorization methods. It also supports real-time updates to the recommender engines, storing engines to disk and reloading them again, and several evaluation measures to compare the accuracy of different recommender system methods. Three command-line programs that offer most of the functionality contained in the library are included.
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
pyuds is a Python library for measuring uncertainty in the Dempster-Shafer theory of evidence. The functionals supported are the Generalized Hartley (GH) uncertainty functional, Generalized Shannon (GS) uncertainty functional, and Aggregate Uncertainty (AU) functional. The library can be utilized either through its API, or through a user-friendly Web interface.
Treba is a commandline tool for training, decoding, and calculating with weighted (probabilistic) finite state automata (WFSA/PFSA). Training algorithms include Baum-Welch (EM), Viterbi training, and Baum-Welch augmented with deterministic annealing. Treba is optimized for speed and numerical stability, and training algorithms can be run multi-threaded on hardware with multiple cores/CPUs. Forward, backward, and Viterbi decoding are supported. Automata for training/decoding are read from a text file, or can be generated randomly or with uniform transition probabilities with different topologies (ergodic or fully connected, Bakis or left-to-right, or deterministic). Observations used for training or decoding are read from text files compatible with AT&T finite state tools and OpenFST.