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About: Bayesian treed Gaussian process models Changes:Fetched by r-cran-robot on 2012-02-01 00:00:11.834310
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About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions. Changes:Now supports OLS and GLS regression and NaiveBayes classification
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About: Boosting Methods for GAMLSS Models Changes:Fetched by r-cran-robot on 2013-04-01 00:00:04.956804
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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:contributed by George Papandreou:
gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg) slight modification to mat/@matFFTN/mvm.m to make it more consistent simple gradient solver using Barzilai-Borwein step size pls/plsBB.m
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About: A python implementation of Breiman's Random Forests. Changes:Initial Announcement on mloss.org.
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About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: The original Random Forests implementation by Breiman and Cutler. Changes:Initial Announcement on mloss.org.
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About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use Changes:Initial Announcement on mloss.org.
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About: Logic Forest Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.077571
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About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents. Changes:
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About: Oblique Random Forests from Recursive Linear Model Splits Changes:Fetched by r-cran-robot on 2012-08-01 00:00:07.607823
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About: Denoising images via normalized convolution Changes:Initial Announcement on mloss.org.
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About: Gradient Boosting Changes:Fetched by r-cran-robot on 2013-05-01 00:00:05.115547
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About: Classification and regression trees Changes:Fetched by r-cran-robot on 2012-02-01 00:00:11.999664
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About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.
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About: Regression Trees with Random Effects for Longitudinal (Panel) Data Changes:Fetched by r-cran-robot on 2013-04-01 00:00:08.040424
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About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms. Changes:Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.
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About: A fast and scalable graph-based clustering algorithm based on the eigenvectors of the nonlinear 1-Laplacian. Changes:
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About: Rule- and Instance-Based Regression Modeling Changes:Fetched by r-cran-robot on 2011-08-28 08:16:03.375532
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