Search Results for "java machine learning library" - Page 2
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Showing 35 open source projects for "java machine learning library"
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Now part of Apache's Mahout machine learning project at http://mahout.apache.org/-- please see there for latest info and code and releases and support!
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XMPP Web Services for Java (XWS4J) is an implementation of machine to machine communication over XMPP. The communicated content is encoded in XML, according to customized definitions of input and output in W3C XML Schemata.
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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.
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TimeSleuth - Temporal Rule Discovery
Temporal and Causal Decision Rules
TimeSleuth discovers temporal decision rules. It also judges the (a)causality of the rules. TimeSleuth can discover rules that involve time: {if (rainy_yesterday = true) then rainy_today = true}, or {if (rainy_tomorrow = true) then rainy_today = true}. -
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JUDGE (Java Utility for Document Genre Eduction) features automatic classification and clustering of documents, optionally as a webservice. The program is written entirely in Java and makes use of the Weka machine learning toolkit.
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HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.
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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.
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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.
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UGLi ML: The Undirected Graphical Library for Machine Learning. Easy, agile learning and inference from structured/relational data using Markov Networks and Conditional Random Fields.
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ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.
