Thinknowlogy is grammar-based software designed to utilize the logic contained within grammar in order to create intelligence through a natural language, which is demonstrated by programming in a natural language, reasoning in a natural language (drawing conclusions, making assumptions (with a self-adjusting level of uncertainty), asking questions (about gaps in the knowledge), and detecting conflicts), and intelligent answering of "is" questions, providing alternative answers as well.
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.
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.
MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms. It addresses the two most common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars), and item prediction from implicit feedback (e.g. from clicks or purchase actions). It contains dozens of recommender engines, including state-of-the-art matrix factorization methods. It also supports real-time updates to the recommender engines, storing engines to disk and reloading them again, and several evaluation measures to compare the accuracy of different recommender system methods. Three command-line programs that offer most of the functionality contained in the library are included.
libagf is a fast, innovative implementation of adaptive or variable-bandwidth kernel-based estimators for statistical classification, PDF estimation, and interpolation/non-linear regression. It is written in C++ and includes simple, command line executables as well as easy-to-use libraries.