Search Results for "matlab text classification"

Text games written in Pascal. The programs can be compiled by Pascal/MT+ (CP/M) and Turbo Pascal (DOS).

TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP full text search engine (http://cwb.sourceforge.net) and a range of statistical functions (factorial analysis, classification, cooccurrency analysis, etc.) based on R packages (http://www.r-project.org). ...

GoIFISH has been developed for the analysis of IFISH (Immunofluorescence + Fluorescence in situ Hybridisation) images, performing nuclear, membrane and spot detection. Users can manually edit segmentation results, perform background adjustments, construct heatmaps, topology maps, and perform cell classification. All results can be exported for further analysis. GoIFISH has been developed in MATLAB, however binaries are provided to run the program outside of the MATLAB environment. Source code is also available for download. To cite this software: "GoIFISH: A system for the quantification of single cell heterogeneity from IFISH images" Trinh A, Rye IH, Almendro V, Helland A, Russness HG, Markowetz F May 2014

ECOC PAK is a C++ Library for the Error Correcting Output Codes classification framework. It supports several coding and decoding strategies as well as several classifiers.

This project aims to build a suite of Natural Language Processing tools. Modules will include corpus indexing and access tools, a part-of-speech tagger, tokenisers, text classification software, etc.

OSU SVM is a Support Vector Machine (SVM) toolbox for the MATLAB numerical environment. The toolbox is used to create models for regression and classification using support vector machines.