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.
PCP (Pattern Classification Program) is a machine learning program for supervised classification of patterns. It runs in interactive and batch modes, and implements the following machine learning algorithms and methods: k-means clustering, Fisher's linear discriminant, dimension reduction using Singular Value Decomposition, Principal Component Analysis, feature subset selection, Bayes error estimation, parametric classifiers (linear and quadratic), pseudo-inverse linear discriminant, k-Nearest Neighbor method, neural networks, Support Vector Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging (committee) classification.
GENESIS (short for GEneral NEural SImulation System) is a general purpose simulation platform that was developed to support the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, simulations of large networks, and systems-level models. It was developed as a research tool to provide a standard and flexible means for constructing structurally realistic models of biological neural systems.
The Discrete Event Calculus Reasoner allows a programmer to add common-sense reasoning capabilities to programs. It supports deduction/temporal projection, abduction/planning, postdiction, and model finding. It allows default reasoning about action, change, space, and mental states. It is based on the event calculus, a comprehensive and highly usable logic-based formalism. It helps applications understand the world, make inferences, adapt to unexpected situations, and be more flexible.
SCIP (Solving Constraint Integer Programs) is a framework for constraint integer programming oriented towards the needs of mathematical programming experts who want to have total control of the solution process and access detailed information down to the guts of the solver. It integrates techniques from mixed integer programming, constraint programming, and SAT solving. It can also be used as a pure MIP solver or as a framework for branch-cut-and-price. In order to use it, you have to link to an LP solver. It currently supports CLP, CPLEX, Mosek, Soplex, and XPress-MP.
Trans (short for Transmuter Programming Language) is an extremely dynamic, biologically-inspired prototyping language providing a framework for experimenting with naturally evolving systems of objects over the net, and for exploring new ideas about recombinant software, code morphing, and evolutionary programming. Trans is also a very capable general-purpose programming language. It's fast, flexible, compact, object-oriented, highly extensible, and easy-to-learn. It can be used for rapid prototyping, or as a scripting language, an embedded language, a network server or client, a system of cooperating network nodes, a real-time control and monitoring system, and more.
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.
METSlib is an object-oriented metaheuristics framework in C++ designed to make implementing or adapting models easy. The model is modular: all the implemented search algorithms can be applied to the same model. METSlib implements the basics of some metaheuristics algorithms, such as Random Restart Local Search, Variable Neighborhood Search, Iterated Local Search, Simulated Annealing, and Tabu Search. For each algorithm, you must implement an objective function, a neighborhood (move manager), and some moves. Tabu Search is one of the fastest ways to generate near-optimal solutions to a wide range of hard combinatorial optimization problems.