Search Results for "machine learning platform" - Page 3
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Showing 64 open source projects for "machine learning platform"
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Very basic cellular automaton implementation in C#. Based upon the "Togetherness" algorithm described at http://www.hermetic.ch/pca/tg.htm.
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This is implementation of parallel genetic algorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library supports the 26 genetic operators. This is cross-platform GA written in С++.
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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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ANT is a lightweight implementation in C of a kind of artificial neural net called Multilayer Perceptron, which uses the backpropagation algorithm as learning method. The package includes an introductory example to start using artificial neural nets.
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Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.
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The Free Connectionist Q-learning Java Framework is an library for developing learning systems. Keywords: qlearning, artificial intelligence, alife, neural nets, neural networks, machine learning, reinforcement learning unsupervised learning agents lejos
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Hipo is a hypothetical computer to facilitate the learning of machine language. The student can use hipo to develop simple programs and understand the internal logic of a computer. There is a plan to implement Donald Knuth's MMIX machine language, also.
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Machine learning toolkit for unsupervised and semi-supervised clustering that demonstrates excellent results on real-world data (see Bekkerman et al. ICML-2005 and ECML-2006).
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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.
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KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
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MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.
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GENet
A genetic algorithm framework for artificial neural networks.
A genetic algorithm framework to allow the evolution of synapse weights and topologies of artificial neural networks. -
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A Visual Studio .NET C++ application can perform machine learning using genetic algorithm, naive bayes, KNN, and Artificial Neural Networks (ANNs) read and processed from any standard ARFF.
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Weka++ is a collection of machine learning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.