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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
Self-Adapting Large-scale Solver Architecture: system for picking numerical algorithms (linear system solving) based on statistical modeling and machinelearning.
ANT is a lightweight implementation in C of a kind of artificial neural net called Multilayer Perceptron, which uses the backpropagation algorithm as learning method. The package includes an introductory example to start using artificial neural nets.
Structlab is a machinelearning C++ framework for structured domains, which provides a toolbox of learning methods and tools for preprocessing and visualization. It also provides a GUI to setup elaborate experiments in a visual and intuitive way.
The Wolfram Machine project is an effort to create a set of documentation and useful modules (both hardware and software) for a computing architecture based on the mathematical theories presented in Steven Wolfram's book _A_New_Kind_of_Science_.
Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.
FLPD is an automatic learning system based on fuzzy prototypes, composed of a C++ library for machinelearning and fuzzy logic and an experimentation framework.
Ruby SVM is a Ruby binding to the very popular and highly useful libsvm library (released under a seperate license) This allows you to effortlessly experiment with machinelearning, in particular Support Vector Machines, in Ruby. SVM's have found use in
AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
Emily is a friendly name for the MachineLearning Environment (MLE). This project is at an early stage of development, and no alpha code is yet available.
Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
The ROSETTA C++ library is a collection of C++ classes and routines that enable discernibility-based empirical modelling and data mining. Comprises useful routines for machinelearning in general and for rough set theory in particular.