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.
ffnet is a fast and easy-to-use feed-forward neural network training solution for Python. You can use it to train, test, save, load, and use an artificial neural network with sigmoid activation functions. Any network connectivity without cycles is allowed (not only layered). Training can be performed with several optimization schemes, including genetic alorithm based optimization. There is access to exact partial derivatives of network outputs versus its inputs. Normalization of data is handled automatically by ffnet.
Fast Artificial Neural Network Library is a neural network library that implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. C++, Perl, PHP, .NET, Python, Delphi, Octave, Pure Data, and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library.
EO is a template-based, ANSI-C++ evolutionary computation library that helps you to write your own stochastic optimization algorithms quickly. Evolutionary algorithms form a family of algorithms inspired by the theory of evolution, and solve various problems. They evolve a set of solutions to a given problem in order to produce the best results. These are stochastic algorithms because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called "metaheuristics". They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.
Algraeph is a tool for manual alignment of linguistic graphs, such as phrase structure trees or dependency structures, where each node corresponds to a subsequence of the analyzed input sentence. It allows you to express the similarity between two graphs by aligning their nodes and attaching relation labels to these alignments. Graphs are read from one or more graphbanks (or treebanks) in the GraphML or Alpino formats. Alignment relations are user-defined and are stored in a simple XML format, which can be used for further processing. The resulting parallel graph corpus is a useful data set for many tasks in computational linguistics and natural language processing.
RebeccaAIML is an enterprise cross platform AIML development platform. RebeccaAIML supports C++, Java, C#, Python, and many other programming languages. It allows AIML development out of the box. RebeccaAIML also comes with an array of AIML administration tools, great documentation, and an Eclipse AIML editor plugin.
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.
Isobel is a framework to build complex information retrieval and analysis systems. Isobel can be functionally divided in two subsytems, Isobel Gatherer (the crawling and filtering subsystem) and Isobel Analyzer (the analysis subsystem). The two subsytems can also be used separately. Isobel Gatherer offers ready-to-use services like content fetching, scheduling, document format conversion, Hyperlink graph storage and analysis, content storage and indexing. A programmer may easily add new services. Isobel Analyzer uses the IBM UIMA architecture to reuse the analysis components developed for this architecture.