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Java Machine Learning Library  v.0.1.6

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, ...

Maja Machine Learning Framework  v.1.0

This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.





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Machine Learning Tools in Java (MLJ)  v.1.0

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.

Bayes Server  v.3.0

Bayes Server™ - Advanced analytical software used in the fields of Machine Learning, Time Series Analysis, Pattern Recognition, Data Mining and Artificial Intelligence.
Supports both Bayesian networks and Dynamic Bayesian networks (for Time Series ...

Weka  v.3.7.9

Weka packahe provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, ...

Weka x64  v.3.7.9

Weka packahe provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, ...

SmartDoc  v.1.0.80.147



Using Machine learning it can learn your existing library structure and without any effort seamlessly classify new documents into existing folders.

You can setup SmartDoc to monitor incoming directory and it will propose where document ...

Neural Network Component (ActiveX)  v.1.0

Artificial Neural System Component is designed for researchers in the fields of machine learning, it can be used to construct Back Propagation Neural Network and to train it with provided samples, then finally recall it with appropriate data.

This ...

SpamBlazer  v.1.2.86.0

It uses machine learning techniques to extract pertinent rules automatically that dictate the legitimacy of an electronic email or otherwise.SpamBlazer Benefits:
1. Theoretically sound spam filtering technology; it is not just another ubiquitous ...

Simbad  v.1.4

It is mainly dedicated to researchers/programmers who want a simple basis for studying Situated Artificial Intelligence, Machine Learning, and more generally AI algorithms, in the context of Autonomous Robotics and Autonomous Agents. It is not intented ...

Neural Networks  v.4.3.7

Inspired by neurons and their connections in the brain, neural network is a representation used in machine learning. After running the back-propagation learning algorithm on a given set of examples, the neural network can be used to predict outcomes ...

Mallet for Mac OS X  v.2.0.7

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document ...

Mallet for Windows  v.2.0.7

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document ...

Data Mining  v.2


Learning of NeoNeuro Data Mining is similar to childs learning. The application makes the same human mistakes which can be seen in chess learning.
NeoNeuro Data Mining is recommended not only for the purpose of students teaching but also ...

Conrad CRF Engine & Gene Caller  v.rc

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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