Showing 133 open source projects for "java machine learning library"

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  • 1
    Yann
    Yann is Yet Another Neural Network. Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
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  • 2
    Artificial neural network with eigenfaces for face recognition
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  • 3
    Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
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  • 4
    Neuroph OCR - Handwriting Recognition
    Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. It's engine derived's from the Java Neural Network Framework - Neuroph and as such it can be used as a standalone project or a Neuroph plug in.
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  • 5
    This project is aimed at making a complete flexible Neural network library in Java. The library works fine with most complex problems.Future targets: Configurable Learning rate and momentum. More details at http://www.w3avatar.com
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  • 6
    Neural - is neural network engine with object-oriented design. Features: - Supports: backpropogation, RPROP algorithms. - Flexible input/outputs framework. - Distributed calculations.
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  • 7
    Java Maths library, providing several mathematical functionalities. It is intended to be a project for learning Java, Maths and project related working. This project was started in Course TAI08B1 at www.ba-karlsruhe.de
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  • 8
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables.
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