Release Notes: This release adds support for previous versions of the .NET Framework, namely .NET 3.5. It also brings Cox's proportional hazard models, several fixes, and a redesigned version of the (Hidden Conditional Random) Fields namespace.
Release Notes: This release adds support for Resilient Backpropagation (RProp) learning for HCRFs, the Goldfarb-Idnani method for solving constrained QP optimization problems, and robust estimation of fundamental matrices through RANSAC, aside from many optimizations in SVM learning and evaluation. This release also includes several bugfixes, corrections, and enhancements to prior versions of the framework, and should be a great update for anyone interested in scientific computing in .NET.
Release Notes: This release introduces the Speeded-Up Robust Features (SURF) detector, Features from Accelerated Segment Test (FAST) corners detector, Limited-memory BFGS method for non-linear optimization, and threshold models for sequence rejection in hidden Markov sequence classifiers.
Release Notes: This release introduces support for independent component analysis, a new audio architecture, and a major refactoring of the hidden Markov models namespace. The new audio architecture can be used in combination with independent component analysis to perform blind source separation of audio signals. The already comprehensive set of kernels for machine learning applications has also been expanded with sparse versions of the Gaussian, Polynomial, Laplacian, Sigmoid, and Cauchy kernels.
Release Notes: Great improvements were made to the documentation. The framework now has support for Continuous density Hidden Markov Models, Gaussian Mixtures, and Non-negative Matrix Factorization.