Open Source Java Machine Learning Software - Page 6

Feature construction and selection are two key factors in the field of Machine Learning (ML). Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. This project makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base, expressed in form of ontology.

OpenIMAJ: The Open toolkit for Intelligent Multimedia Analysis in Java. OpenIMAJ contains a large collection of pure-Java classes for analysing multimedia documents, from tools for extracting image features, to tools for analysing web pages.

Optex Analyzer is a software to analyze and compare algorithms to solve approximately optimization problems. It has a GUI that allows select a set of input files containing raw algorithm results. The analysis is shown with tables and charts.

Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein applications configure an entire deployment of the system. This includes implementations of key interface classes which implement the batch, speed, and serving logic. Applications package and deploy their implementations with each instance of the layer binaries. Each of these is a runnable Java .jar which starts all necessary services.

This project aims to develop a method to identify communities in a social network according to some point of view.

R-Portfolio - breakeven optimally diversified investment portfolio. Оптимально диверсифицированный инвестиционный портфель.

An experimental unsupervised learning method for improvising jazz melodies, based on restricted Boltzmann machines (RBMs) layered into Deep Belief Networks (forms of neural networks).

Reconcile is an open source research platform for coreference resolution. It combines a large number of open source NLP components and provides extension points for researchers to plug in additional features and techniques.

This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.

Scene is a computer vision framework that performs background subtraction and object tracking, using two traditional algorithms and three more recent algorithms based on neural networks and fuzzy classification rules. For each detected object, Scene sends TUIO messages to one or several client applications. The present release features GPU accelerated versions of all the background subtraction methods and morphological post processing of the object blobs with dilation and erosion filters, implemented in OpenCL. The framework was mainly designed as a toolkit for the rapid development of interactive art projects that explore dynamics of complex environments. The Scene GUI runs and compiles under Windows, Linux, and MacOS X, and is available in both 32 bit and 64 bit versions.

A java tool for anytime and interactive sequence mining. Aims at providing users with a way of analyzing her activity traces and extract activity schemes from them.

SAIM allows to interlink knowledge bases in the Semantic Web. It focuses on instance matching of very large knowledge bases available as SPARQL endpoints. SAIM uses machine learning techniques and is compatible with SILK.

Spec is a voice control based on the libraries of Sphinx-4.

A Java application that tries to learn the ontology of sport articles in German newspapers.

SuRankCo is a machine learning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet. For more details about SuRankCo and its functioning, please see "SuRankCo: Supervised Ranking of Contigs in de novo Assemblies" Mathias Kuhring, Piotr Wojtek Dabrowski, Andreas Nitsche and Bernhard Y. Renard (http://www.biomedcentral.com/1471-2105/16/240/abstract) PLEASE NOTE, it is recommended to read the paper and the readme.txt file before using SuRankCo. Update Jun2015: * Minor changes to enable BAM support. Update Feb2014: * Added support for FASTA/SAM assemblies in addition to ACE/FASTQ(QUAL). NOTE: features of FASTA/SAM assemblies do not include BaseCount, BaseSeqmentCount and ContigQualities yet.

Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.

Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%.

A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/

Text annotation application (Tapp) is a stand alone software component that facilitates the quick annotation of text files for the purpose of creating labelled data for training, testing, and deploying machine learning models

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}.

Este projeto objetiva a criação de um Toolbox para utilização de algoritmos de Computação Bioinspirada. O Toobox oferece uma vasta coleção de algoritmos de ferramentas para o projeto de Algoritmos Genéticos e Redes Neurais.

This Project is to make a robotic platform and Soft Brain for a self learning research robot. For making it modular we are using OSGI with rosjava javacv.

Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.

UnBFuzzy is an API for fuzzy-logic and learning algorithms for fuzzy systems.