About: Data-efficient policy search framework using probabilistic Gaussian process models

Initial Announcement on mloss.org.

About: PRoNTo is freely available software and aims to facilitate the interaction between the neuroimaging and machine learning communities. The toolbox is based on pattern recognition techniques for the analysis of neuroimaging data. PRoNTo supports the analysis of all image modalities as long as they are NIfTI format files. However, only the following modalites have been tested for version 1.1: sMRI, fMRI, PET, FA (fractional anisotropy) and Beta (GLM coefficients) images.

Initial Announcement on mloss.org.

About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of three-way-tensors and determination of their rank, includes example applications.

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

Initial Announcement on mloss.org.

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables.

Initial Announcement on mloss.org.

About: Regularization for semiparametric additive hazards regression

Fetched by r-cran-robot on 2018-09-01 00:00:03.378832

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.

• minor bugfix

About: A comprehensive data mining environment, with a variety of machine learning components.

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.

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.

• New and improved HDDM model with the following changes:
  • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
  • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
  • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
  • The old model has been renamed to HDDMTransformed.
  • HDDMRegression and HDDMStimCoding are also using this model.
• HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
• Improved online documentation at http://ski.clps.brown.edu/hddm_docs
• A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
• Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
• Maximum likelihood optimization.
• Many bugfixes and better test coverage.
• hddm_fit.py command line utility is depracated.

About: R genetic programming framework

Fetched by r-cran-robot on 2013-04-01 00:00:08.163887

About: Pam

Fetched by r-cran-robot on 2013-04-01 00:00:06.709586

About: Generalized linear and additive models by likelihood based boosting

Fetched by r-cran-robot on 2013-04-01 00:00:04.893311

About: Classification and visualization

Fetched by r-cran-robot on 2013-04-01 00:00:05.722314

About: A Toolkit for Recursive Partytioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.838561

About: Feed-forward Neural Networks and Multinomial Log-Linear Models

Fetched by r-cran-robot on 2013-04-01 00:00:06.544403

About: Graphical user interface for data mining in R

Fetched by r-cran-robot on 2013-04-01 00:00:07.700426

About: Regularization paths for SCAD- and MCP-penalized regression models

Fetched by r-cran-robot on 2013-04-01 00:00:06.449164

About: Lasso and elastic-net regularized generalized linear models

Fetched by r-cran-robot on 2013-04-01 00:00:05.081872

About: The open-source C-package 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).

Initial Announcement on mloss.org.