About: Breiman and Cutler's random forests for classification and regression

Fetched by r-cran-robot on 2013-04-01 00:00:07.638240

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

This is a major release, with several novelties, improvements and fixes, among which:

• step-size two-point adaptaion scheme for improved performances in some settings, ref #88

• important bug fixes to the ACM surrogate scheme, ref #57, #106

• simple high-level workflow under Python, ref #116

• improved performances in high dimensions, ref #97

• improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

• elitist mechanism for forcing best solutions during evolution, ref 103

• new legacy plotting function, ref #110

• optional initial function value, ref #100

• improved C++ API, ref #89

• Python bindings support with Anaconda, ref #111

• configure script now tries to detect numpy when building Python bindings, ref #113

• Python bindings now have embedded documentation, ref #114

• support for Travis continuous integration, ref #122

• lower resolution random seed initialization

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser - ferdinand.kaiser@tut.fi)

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.

In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.

About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces.

• Reset point indices of results with distances exceeding threshold (#23, #24)
• Fine tune the find_package() capability and add uninstall target (#22)
• Fixed compiler warning (#18)
• Added OpenMP support (#20, #21)
• Build type tuning (#19)
• Fix: terminal comma in enum requires C++11
• Fix UBSAN error calculating maxNodeCount (#16, #17)
• Fixed tiny (yet significant) error in the Python doc strings (#15)
• Compile static lib with PIC (#14)
• Added configure scripts for full catkinization
• Catkinization of libnabo (following REP136)
• Update README.md Added Simon as the maintainer.
• [test] use CLOCK_PROF for NetBSD build
• Fixed CppCheck warning. Fix broken install when doxygen is not found
• Fix cmake stylistic issue
• Make python install respect custom CMAKE_INSTALL_PREFIX
• Fix broken install when doxygen is not found

About: Gradient Boosting

Fetched by r-cran-robot on 2018-09-01 00:00:05.199020

About: Bayesian treed Gaussian process models

Fetched by r-cran-robot on 2012-02-01 00:00:11.834310

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

• Distributed version of xgboost that runs on YARN, scales to billions of examples

• Direct save/load data and model from/to S3 and HDFS

• Feature importance visualization in R module, by Michael Benesty

• Predict leaf index

• Poisson regression for counts data

• Early stopping option in training

• Native save load support in R and python

• xgboost models now can be saved using save/load in R

• xgboost python model is now pickable

• sklearn wrapper is supported in python module

• Experimental External memory version

About: Open Source Machine Learning Server

• Single machine version for small-to-medium scale deployments
• Integrated GraphChi (disk-based large-scale graph computation) and algorithms: ALS, CCD++, SGD, CLiMF
• Improved runtime for training and offline evaluation
• Bug fixes

See release notes - https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801

About: A Tool for Measuring String Similarity

This release fixes the incorrect implementation of the bag distance.

About: BCPy2000 provides a platform for rapid, flexible development of experimental Brain-Computer Interface systems based on the BCI2000.org project. From the developer's point of view, the implementation [...]

Bugfixes and tuneups, and an expanded set of (some more-, some less-documented, optional tools)

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method.

• Supports weighted lasso (dalsqal1.m, dallral1.m)
• Supports weighted squared loss (dalwl1.m)
• Bug fixes (group lasso and elastic-net-regularized logistic regression)

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.

• Experiments can now be invoked from the command line
• Experiments can now be "scripted"
• MMLF Experimenter contains now basic module for statistical hypothesis testing
• MMLF Explorer can now visualize the model that has been learned by an agent

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

CHANGES IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

• spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
• fabia for non-negative factorization (parameter: non_negative);
• changed to C and removed dependencies to Rcpp;
• improved update for lambda (alpha should be smaller, e.g. 0.03);
• introduced maximal number of row elements (lL);
• introduced cycle bL when upper bounds nL or lL are effective;
• reduced computational complexity;
• bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

• New option nL: maximal number of biclusters per row element;
• Sort biclusters according to information content;
• Improved and extended preprocessing;
• Update to R2.13

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website.

New in toolbox

• Using GPU in Backpropagation
• Revision of some demo scripts
• Function approximation with multiple outputs
• Feature extraction with GRBM in first layer

About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining.

1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "--score-for-class C" causes scores to be computed relative to a given (positive) class. For two-class problems. Fixed bug in example sampling code (--sample n) Fixed bug keeping old-style example formats (terminated by dot) from working. More code restructuring.

About: Improved Predictors

Fetched by r-cran-robot on 2013-04-01 00:00:05.613011

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999