About: Big Random Forests

Fetched by r-cran-robot on 2015-11-01 00:00:04.072762

About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...]

• Add bib
• Add code of nystrom without orthogonalization
• Add accuracy quality measure
• Add more running scripts

About: Multivariate partitioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.387032

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 Java Toolbox for Scalable Probabilistic Machine Learning.

• Added sparklink module implementing the integration with Apache Spark. More information here.
• Fluent pattern in latent-variable-models
• Predefined model implementing the concept drift detection

Detailed information can be found in the toolbox's web page

About: An open source Java software providing collaborative filtering algorithms.

Initial Announcement on mloss.org.

About: HSSVM is a software for solving multi-class problem using Hyper-sphere Support Vector Machines model, implemented by Java.

1. From this version, the version number is normalized to hssvm1.0.1;
2. In this version, we delete the features about running parameter searching and run-all from Ant script, that is, commands "ant search-param" and "ant run-all" which exist in previous version are no longer available, and they are replaced with commands "svm search conf" and "svm runall conf", both of them are used on Linux(or all other POSIX systems).If you want to use this program on Windows, the cygwin is required to be installed.

About: A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and a tree data structure. This library is useful for efficient Kernel Density [...]

Initial Announcement on mloss.org.

About: A header-only C++ library for solving large scale eigenvalue problems

• Fixed regressions in v0.6.1 on some edge cases
• Improved the accuracy of restarting processes in SymEigsSolver and GenEigsSolver
• Code and documentation cleanup

About: ALGLIB is an open source numerical analysis library distributed under GPL 2+. It implements both general numerical algorithms and machine learning algorithms. ALGLIB can be used from C#, C++, FreePascal, VBA and other languages. It is the only numerical analysis library which uses automatic translation to generate source code written in different programming languages with 100% identical functionality.

1. New algorithms implemented: exact and approximate nearest neighbor search using kd-trees, new multidimensional scattered data interpolation/fitting algorithm with O(N·logN) complexity (modified Shepard's method with fast k-NN queries), linear algebra algorithms

2. faster QR/LQ, matrix inversion, condition number estimation.

3. Minor fixes

About: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)

Fetched by r-cran-robot on 2012-02-01 00:00:11.194183

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Initial Announcement on mloss.org.

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

• added ElasticNets as a regularization option
• fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
• added a couple of I/O optimizations

About: Scalable tensor factorization

• Improve (speed up) initialization of A by summation

About: This is the source code of the mloss.org website.

Now works with newer django versions and fixes several warnings and minor bugs underneath. The only user visible change is probably that the subscription and bookmark buttons work again.

About: The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library that has been specifically designed for real-time gesture recognition. It features a large number of machine-learning algorithms for both classification and regression in addition to a wide range of supporting algorithms for pre-processing, feature extraction and dataset management. The GRT has been designed for real-time gesture recognition, but it can also be applied to more general machine-learning tasks.

Added Decision Tree and Random Forests.

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

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

About: Efficient and Flexible Distributed/Mobile Deep Learning Framework, for python, R, Julia and more

This version comes with Distributed and Mobile Examples