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About: Tapkee is an efficient and flexible C++ template library for dimensionality reduction. Changes:Initial Announcement on mloss.org.
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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. Changes:Initial Announcement on mloss.org.
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About: GPgrid toolkit for fast GP analysis on grid input Changes:Initial Announcement on mloss.org.
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About: Fast Multidimensional GP Inference using Projected Additive Approximation Changes:Initial Announcement on mloss.org.
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About: Ankus is an open source data mining / machine learning based MapReduce that supports a variety of advanced algorithms. Changes:Initial Announcement on mloss.org.
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About: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. Changes:Moved from CRAN to Bioconductor. Improved handling of molecules, visualization and examples.
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About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition Changes:
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About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used
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About: Test submission. Is MLOSS working? Changes:Initial Announcement on mloss.org.
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About: CIlib is a library of computational intelligence algorithms and supporting components that allows simple extension and experimentation. The library is peer reviewed and is backed by a leading research group in the field. The library is under active development. Changes:Initial Announcement on mloss.org.
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About: The mission of this project is to build and support a community interested in machine learning and machine intelligence based on modeling the neocortex and the principles upon which it works. Changes:Initial Announcement on mloss.org.
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About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization. Changes:
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About: This evaluation toolkit provides a unified framework for evaluating bag-of-words based encoding methods over several standard image classification datasets. Changes:Initial Announcement on mloss.org.
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About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of three-way-tensors and determination of their rank, includes example applications. Changes:Initial Announcement on mloss.org.
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About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships. Changes:
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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.
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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. Changes:Initial Announcement on mloss.org.
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About: The toolbox from the paper Near-optimal Experimental Design for Model Selection in Systems Biology (Busetto et al. 2013, submitted) implemented in MATLAB. Changes:Initial Announcement on mloss.org.
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About: Regularization for semiparametric additive hazards regression Changes:Fetched by r-cran-robot on 2018-09-01 00:00:03.378832
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About: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Changes:Initial Announcement on mloss.org.
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