SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
SIP provides image processing, pattern recognition, and computer vision routines for SciLab, a Matlab-like matrix-oriented programming environment. SIP is able to read/write images in almost 90 major formats, including JPEG, PNG, BMP, GIF, FITS, and TIFF. It includes routines for filtering, segmentation, edge detection, morphology, curvature, fractal dimension, distance transforms, multiscale skeletons, and more.
Wandora is a general purpose data extraction, management, and publishing application based on Topic Maps and Java. Wandora has a graphical user interface, layered presentation of knowledge, several data storage options, rich data extraction, import and export capabilities, and an embedded HTTP server that enables dynamic publication of Topic Maps. Wandora is well suited for rapid ontology construction and knowledge mashups.