Open Source Java Machine Learning Software for Windows - Page 6
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Java Machine Learning Software for Windows
View 76 business solutionsBrowse free open source Java Machine Learning Software for Windows and projects below. Use the toggles on the left to filter open source Java Machine Learning Software for Windows by OS, license, language, programming language, and project status.
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jFAN2 project develops a Java API to use a neural network (neuro-fuzzy) called FAN (Free Associative Neurons). Its an reprogramed version of jFAN "https://sourceforge.net/projects/jfan/".
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jLDADMM
A Java package for the LDA and DMM topic models
The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. See the usage of jLDADMM in its website at http://jldadmm.sourceforge.net/ -
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jPrediction
Open project ternary classifier of the machine learning
Open project ternary classifier of the machine learning -
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libVMR
VMR - machine learning library
libVMR is a class library written in Java which implements code generator for group method of data handling - GMDH. The library is intended for users, with machine learning skills. libVMR provides an effective framework for the research and development of data mining and predictive analytics. libVMR is based on the most popular neural network model with a higher generalization ability from kernel tricks - vector machine by Reshetov (VMR). The library has been designed to learn from data sets. Typical applications here are pattern recognition ( binary classification). -
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Moara is a biological text mining tool and consists of a Java library and some auxiliary MySQL databases for gene/protein training and extraction of mentions and its further normalization and disambiguation.
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This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.
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weka outlier is an implementation of outlier detection algorithms for WEKA. CODB (Class Outliers: Distance-Based) Algorithm is the first algorithm developed using WEKA framework.
