Showing Items 191200 of 628 on page 20 of 63: First Previous 15 16 17 18 19 20 21 22 23 24 25 Next Last
About: C++ software for statistical classification, probability estimation and interpolation/nonlinear regression using variable bandwidth kernel estimation. Changes:New in Version 0.9.8:

About: STK++: A Statistical Toolkit Framework in C++ Changes:Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix

About: a parallel LDA learning toolbox in MultiCore Systems for big topic modeling. Changes:Initial Announcement on mloss.org.

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation. Changes:Initial Announcement on mloss.org.

About: This is a library for solving nuSVM by using Wolfe's minimum norm point algorithm. You can solve binary classification problem. Changes:Initial Announcement on mloss.org.

About: A MATLAB toolbox for defining complex machine learning comparisons Changes:Initial Announcement on mloss.org.

About: This provide a semisupervised learning method based cotraining for RGBD object recognition. Besides, we evaluate four stateoftheart feature learing method under the semisupervised learning framework. Changes:Initial Announcement on mloss.org.

About: LogRegCrowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979). Changes:Initial Announcement on mloss.org. Added GitHub page.

About: This library implements the OptimumPath Forest classifier for unsupervised and supervised learning. Changes:Initial Announcement on mloss.org.

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over datadependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. Changes:improved testing, improved documentation, windows compatibility, more algorithms
