Machine Learning

The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).

LPCforSOS is a machine learning framework with a special focus on structured output spaces and pairwise learning. It supports currently multiclass, ordinal, hierarchical, multi-label and label ranking classification settings.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.


Platforms: Windows, Mac, Solaris, Linux

PepArML: An unsupervised, model-free, combining peptide identification arbiter for tandem mass spectra via machine learning.


Platforms: Mac, BSD, Linux

A collection of cheminformatics and machine-learning software written in C++ and Python. The core algorithms and data structures are written in C++. Wrappers are provided to use the toolkit from either Python or Java. Additionally, the RDKit distribution includes a PostgreSQL-based cartridge...


Platforms: Windows, Mac, BSD, Linux

The TreeQ package is a set of C-language applications that implement aautomatic machine learning algorithm based on a tree-structured classifier. This approach is particularly effective for high-dimensional continuous data such as audio and video.


Platforms: Windows, Mac, Linux

T-Rex (Trainable Relation Extraction) is a highly configurable machine learning-based Information Extraction from Text framework, which includes tools for document classification, entity extraction and relation extraction.


Platforms: Windows, Mac, Linux

A collection of plug-in algorithms for the WEKA machine learning workbench including artificial neural network (ANN) algorithms, and artificial immune system (AIS) algorithms.


Platforms: Windows, Mac, Linux

Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.


Platforms: Windows, Mac, Linux

Divvy is an application for performing unsupervised machine learning and visualization. We focus on the clustering (separating data into groups) and dimensionality reduction (finding low dimensional structure in high dimensional data) subfields of machine


Platforms: Mac

Weka is a comprehensive open source Machine Learning toolkit, written in Java at the University of Waikato, New Zealand. These functions provide a basic Matlab interface to Weka allowing you to transfer data back and forth and access major Weka features, such as training Classifiers. They have...


Platforms: Matlab

Frequent Itemset search is needed as a part of association mining in Data mining research field of Machine Learning.This code gives you upto the frequent k-itemset as output.You may want to change two things in MAIN file as per your need:First one is 'A': the input matrix with each row as a...


Platforms: Matlab

A Genetic Programming workbench to analyze and solve problems with this machine learning technique. Bloody cool. Oh, and it's free. GP.Lab uses the ESC GP kernel to automatically solve various problems currently including: Mathematical function regression Santa-Fe Ant problem RoboRail The...


Platforms: Mac

The DL-Learner software learns concepts in Description Logics (DLs) from examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. It extends Inductive Logic Programming to Descriptions Logics and the Semantic Web. The goal of[b] DL-Learner is to provide a...


Platforms: Mac

Iracema is a Named Entity Recognition and Classification (NERC) library that aims to provide algorithms and commonly used functionality for both implementing and evaluating NERC systems. It is implemented in Java. Iracema features: * A Flexible architecture for implementing and evaluating NERC...


Platforms: Mac

AndroMeta is a software system which spans a diverse range of fields including: machine learning, knowledge bases, and artificial intelligence in general, distributed, concurrent, and GPU computing, languages and language design, modeling and simulation, and more * all of which are unified into a...


Platforms: Mac

PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. It is tested on Linux and Mac OS X. Features?*A* * Classifiers: support vector machines, nearest neighbor, ridge regression * Multi-class methods...


Platforms: Mac

A classifier is a machine learning tool that will take data items and place them into one of k classes. A probabilistic classifier, like this one, can also give a probability distribution over the class assignment for a data item. This software is a Java implementation of a maximum entropy...


Platforms: Mac

dysii is a C++ library provides a collection of classes useful for machine learning applications. Features are added to the library as needed, so that it does lack some completeness. What is included, however, is well documented and tested, and may be considered fairly sound for research...


Platforms: *nix

PyML is an interactive object oriented framework for machine learning written in python. PyML focuses on kernel classifiers, providing tools for feature selection, model selection, and methods for assessing classifier performance.


Platforms: *nix