About: Genetic Algorithm for Curve Fitting

Fetched by r-cran-robot on 2012-10-01 00:00:04.684941

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Initial Announcement on mloss.org.

About: The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...]

Initial Announcement on mloss.org.

About: Shrinkage Discriminant Analysis and CAT Score Variable Selection

Fetched by r-cran-robot on 2012-02-01 00:00:11.559491

About: Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The demand for such methods is increasing with the availability and size of co-occurring observations in computational biology, open data initiatives, and in other domains. We provide practical, open access implementations of general-purpose algorithms that help to realize the full potential of these information sources.

Three independent modules (drCCA, pint, MultiWayCCA) have been added.

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

This version introduces a number of new features:

• The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

• The parser now outputs dependency labels along with the backbone structure.

• As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

• The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Moved project to GitHub.

About: A desktop planetarium and sky map program which shows the sky from anywhere on Earth at any time.

Removed erroneous topocentric code. Increased maximum zoom for detail on planets.

About: This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares.

• fixed several bugs
• drastic speed-up of computation time

About: Generalized Ridge Regression (with special advantage for p >> n cases)

Fetched by r-cran-robot on 2018-05-01 00:00:05.929954

About: C-MixSim is an open source package written in C for simulating finite mixture models with Gaussian components. With a vast number of clustering algorithms, evaluating performance is important. C-MixSim provides an easy and convenient way of generating datasets from Gaussian mixture models with different levels of clustering complexity. C-MixSim is released under the GNU GPL license.

Initial Announcement on mloss.org.

About: PLearn is a large C++ machine-learning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...]

Initial Announcement on mloss.org.

About: SVQP1 and SVQP2 are QP solvers for training SVM.

Initial Announcement on mloss.org.

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data.

• 5 more applications, i.e. DFI, MicroRazerS, PairAlign, SeqCons, TreeRecon
• stable release of RazerS supporting paired-end read mapping and configurable sensitivity
• new alignment algorithms, e.g. banded, configurable alignments (overlap, semi-global, ...)
• realignment algorithm
• NGS data structures and formats, e.g. SAM, Amos, ...
• new alphabets, e.g. Dna with base call qualities, profile characters
• auxiliary data structures and algorithms, e.g. double ended queue, command line parser
• positional scores
• CMake support

About: General purpose Java Machine Learning library for classification, regression, and clustering.

See github release tab for change info

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

improved testing, improved documentation, windows compatibility, more algorithms

About: This toolbox implements models for Bayesian mixed-effects inference on classification performance in hierarchical classification analyses.

In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.

About: Piqle (Platform for Implementing Q-Learning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platform-independent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making.

Initial Announcement on mloss.org.