Search Results for "machine learning"

AMBIT offers a cheminformatics data management for chemical substances, structures and nanomaterials. Flexible structure, similarity and study queries storage, descriptor calculation and predictive models building via REST web services.User interface for creating read across assessment and generating reports, aligned with regulatory and industrial requirements. Command line applications also available. Integration with third party tools and databases.

This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ...

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

TreeLiker is a collection of fast algorithms for working with complex structured data in relational form. The data can, for example, describe large organic molecules such as proteins or groups of individuals such as social networks or predator-prey networks etc. The algorithms included in TreeLiker are unique in that, in principle, they are able to search given sets of relational patterns exhaustively, thus guaranteeing that if some good pattern capturing an important feature of the problem...

Platform for parallel computation in the Amazon cloud, including machine learning ensembles written in R for computational biology and other areas of scientific research. Home to MR-Tandem, a hadoop-enabled fork of X!Tandem peptide search engine.

BorderFlow implements a general-purpose graph clustering algorithm. It maximizes the inner to outer flow ratio from the border of each cluster to the rest of the graph.

GAME stays for Generic Architecture based on Multiple Experts. Its main purpose is to make easy prototyping, test and release of prediction systems. Released by IASC group, university of Cagliari

The aim of the BioWeka project is to add bioinformatics functionalities such as e.g. alignments to the popular machine learning framework Weka.

Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.