About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of threewaytensors and determination of their rank, includes example applications. Changes:Initial Announcement on mloss.org.

About: This is the core MCMC sampler for the nonparametric sparse factor analysis model presented in David A. Knowles and Zoubin Ghahramani (2011). Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling. Annals of Applied Statistics Changes:Initial Announcement on mloss.org.

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2X*betay^2 + lambda*sumbeta_i, where X and y are problem data and beta and lambda are variables. Changes:Initial Announcement on mloss.org.

About: Regularization for semiparametric additive hazards regression Changes:Fetched by rcranrobot on 20180201 00:00:03.791469

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in:  Ellipsoidal multiple instance learning  difference of convex functions algorithms for sparse classfication  Contextual bandits upper confidence bound algorithm (using GP)  learning output kernels, that is kernels between the labels of a classifier. Changes:

About: A comprehensive data mining environment, with a variety of machine learning components. Changes:Modifications following feedback from Knime main Author.

About: A descriptive and programming language independent format and API for the simplified configuration, documentation, and design of computer experiments. Changes:Initial Announcement on mloss.org.

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Changes:

About: R genetic programming framework Changes:Fetched by rcranrobot on 20130401 00:00:08.163887

About: Pam Changes:Fetched by rcranrobot on 20130401 00:00:06.709586

About: Generalized linear and additive models by likelihood based boosting Changes:Fetched by rcranrobot on 20130401 00:00:04.893311

About: Classification and visualization Changes:Fetched by rcranrobot on 20130401 00:00:05.722314

About: A Toolkit for Recursive Partytioning Changes:Fetched by rcranrobot on 20130401 00:00:06.838561

About: Feedforward Neural Networks and Multinomial LogLinear Models Changes:Fetched by rcranrobot on 20130401 00:00:06.544403

About: Graphical user interface for data mining in R Changes:Fetched by rcranrobot on 20130401 00:00:07.700426

About: Regularization paths for SCAD and MCPpenalized regression models Changes:Fetched by rcranrobot on 20130401 00:00:06.449164

About: Lasso and elasticnet regularized generalized linear models Changes:Fetched by rcranrobot on 20130401 00:00:05.081872

About: The opensource Cpackage fastICA implements the fastICA algorithm of Aapo Hyvarinen et al. (URL: http://www.cs.helsinki.fi/u/ahyvarin/) to perform Independent Component Analysis (ICA) and Projection Pursuit. fastICA is released under the GNU Public License (GPL). Changes:Initial Announcement on mloss.org.

About: Heteroscedastic Discriminant Analysis Changes:Fetched by rcranrobot on 20130401 00:00:05.551691

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, NadarayaWatson estimator); (3) generative models for random networks (smallworld, scalefree, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition. Changes:JProGraM 13.2  CHANGE LOG Release date: February 13, 2012 New features:  Support for Fiedler random graphs/random field models for largescale networks (ninofreno.graph.fiedler package);  Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).
