Search Results for "kernel-ntfs"

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities.

ViennaMath provides a symbolic math kernel which can be used either for compile-time processing, or for run-time evaluation. Unlike other symbolic math implementations, ViennaMath aims at providing a fast math layer for use with numerical methods such as the finite element method (cf. ViennaFEM)

ViennaFEM is a library-centric implementation of the finite element method in C++. It features a symbolic math kernel, which manipulates the strong or weak form of the problem and automatically derives the discrete form. ViennaFEM is built on top of the following libraries: ViennaMath provides the symbolic math kernel, ViennaGrid (with ViennaData) allows for a generic grid handling and quantity storage, while ViennaCL provides the linear solvers and GPU acceleration.

The Optimized Sparse Kernel Interface (OSKI) Library provides automatically tuned sparse matrix kernels, for use by solver libraries and applications. OSKI is part of the BeBOP project on performance tuning and analysis at U.C. Berkeley. (Go Bears!)