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ALGLIB 2.4.0

Sergey Bochkanov — Wed, 10 Mar 2010 14:07:35 -0000

ALGLIB is an open source numerical analysis library distributed under GPL 2+. It implements both general numerical algorithms and machine learning algorithms.

As for machine learning algorithms, ALGLIB implements: MLPs, early stopping ensembles, LDA, PCA, k-means++, several other data analysis algorithms. As for general numerical algorithms, ALGLIB implements linear algebra functions (subset of LAPACK), linear solvers, ODE solvers, nonlinear optimization, interpolation/fitting, integration, fast transforms.

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. So, C# version is written entirely in C#, C++ version is entirely C++, but they provide exactly the same interface.

dlib ml 17.26

Davis King — Sun, 07 Mar 2010 21:37:42 -0000

A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

The library provides efficient implementations of the following algorithms:

support vector machines for classification
relevance vector machines for regression and classification
reduced set approximation of SV decision surfaces
online kernel RLS regression
online kernelized centroid estimation/one class classifier
online SVM classification
kernel k-means clustering
radial basis function networks
kernelized recursive feature ranking
Bayesian network inference using junction trees or MCMC

The library also comes with extensive documentation and example programs that walk the user through the use of these machine learning techniques.

dlib also comes with a fast matrix library that lets the user use a simple Matlab like syntax. It is also capable of using BLAS libraries such as ATLAS or the Intel MKL when available. Additionally, the use of BLAS is transparent to the user, that is, the dlib matrix object uses BLAS internally to optimize all the various forms of matrix multiplication while still allowing the user to use a simple Matlab like syntax.

Continuous Time Bayesian Network Reasoning and Learning Engine 1.0.2

Christian Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu — Fri, 05 Mar 2010 23:45:20 -0000

CTBN-RLE is a continuous time Bayesian network reasoning and learning engine. A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuous-time Markov process. This software provides libraries and executables for most of the algorithms developed for CTBNs. For learning, CTBN-RLE implements structure and parameter learning for both complete and partial data. For inference, it implements exact inference and Gibbs and importance sampling approximate inference for any type of evidence pattern. Additionally, the library supplies visualization methods for graphically displaying CTBNs or trajectories of evidence.

Error Correcting Output Codes Library 0.1

Sergio Escalera, Oriol Pujol, Petia Radeva — Fri, 05 Mar 2010 16:49:12 -0000

In this work we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs hamming, euclidean, inverse hamming, laplacian, Beta-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.

PyBrain 0.3

Wed, 03 Mar 2010 15:00:08 -0000

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and evolution strategies like CMA-ES, NES or FEM.

Jstacs 1.3.1

Jens Keilwagen, Jan Grau, Andre Gohr — Tue, 02 Mar 2010 14:15:46 -0000

Sequence analysis is one of the major subjects of bioinformatics. Several existing libraries combine the representation of biological sequences with exact and approximate pattern matching as well as alignment algorithms. We present Jstacs, an open source Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an efficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches for parameter learning. Using Jstacs, classifiers can be assessed and compared on test datasets or by cross-validation experiments evaluating several performance measures. Due to its strictly object-oriented design Jstacs is easy to use and readily extensible.

NaN toolbox 2.1.0

alois schloegl — Sun, 28 Feb 2010 22:00:21 -0000

The NaN-toolbox provides a number of statistics functions and machine learning methods for the use with Octave and Matlab. The functions can handle data with missing values encoded as NaNs, weighting of data samples, and multi-class classification (using a one-versus-rest scheme). There is a common interface to a number of different classification methods (including FDA, LDA, Naive Bayes, QDA, RDA, sparse classifiers, interfaces to some SVMs, regression/PLS, Wiener-Hopf).

K tree 0.1.1

Lance De Vine, Chris De Vries, Shlomo Geva — Sat, 27 Feb 2010 02:41:46 -0000

K-tree is a tree structured clustering algorithm. It is also refered to as a Tree Structured Vector Quantizer (TSVQ). The goal of cluster analysis is to group objects based on similarity. Each object in a K-tree is represented by an n-dimensional vector. All vectors in the tree must have the same number of dimensions. At the K-tree 0.1 release the only similarity measure for vectors is Euclidean distance.

The algorithm is a hybrid of the B+-tree and k-means algorithms. It uses a similar tree structure to the B+-tree and uses k-means to perform splits. The tree forms a nearest neighbour search tree. Unlike k-means the number of clusters does not need to be specified upfront. However, a tree order must be specified that restricts how many vectors can be stored in any node. Each level of the tree produces a different number of clusters.

LIBSVM 2.9

ChihChung Chang, ChihJen Lin — Sat, 27 Feb 2010 01:09:23 -0000

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include

o Different SVM formulations

o Efficient multi-class classification

o Cross validation for model selection

o Probability estimates

o Weighted SVM for unbalanced data

o Both C++ and Java sources

o GUI demonstrating SVM classification and regression

o Python, R (also Splus), MATLAB, Perl, Ruby, Weka, CLISP and LabVIEW interfaces. C# .NET code is available. It's also included in some learning environments: YALE and PCP.

o Automatic model selection which can generate contour of cross valiation accuracy.

GPUML GPUs for kernel machines 4

Balaji Vasan Srinivasan, Qi Hu, Ramani Duraiswami — Fri, 26 Feb 2010 18:12:46 -0000

Kernel machines are integral part of many learning approaches. We have identified 3 key computations encountered in these algorithms. 1. Weighted summation of kernel function (ex. kernel density estimation) 2. Kernel matrix-vector product in an iterative algorithm (ex. kernel regression) 3. Operations on the kernel matrix (ex. kernel PCA)

In this software, we accelerate each of these on a GPU. See the documentation for more details, and demo to see the speedups.

System requirement: Cuda 2.2+, Visual studio 2008 (if used in Windows)