About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

• Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

• Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

• Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013

About: Learns gradient boosted regression tree ensembles in parallel on shared memory or cluster systems

Initial Announcement on mloss.org.

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels.

Added missing dataset function (thanks to Hao Wu)

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.

About: A Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems.

• Added deflation scheme to compute multiple principal components
• Several internal runtime and memory optimizations
• API change: sparsePCA.m is now used to compute multiple components; use computeTradeOffCurve.m to reproduce the examples in the NIPS paper

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

• Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
• Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
• Refactored tests for data formats (e.g. PlainText and JSON tests).
• Added visitor design pattern for graph-based functions (e.g. initialization and processing).
• Documented parameters of Processor implementations.

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: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

• Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
• Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
• New class: CrossValidation - evaluator of trainable cells through cross-validation
• New class: RMSE - calculation of Root Mean Squared Error score
• Data objects can be compared and used in collections
• ArffReader can read from a user-provided java.io.InputStream
• More convenient use of parameters (setting values)
• More convenient use of data objects and data types (construction, type casting)
• Other minor improvements to existing classes
• Javadoc extended

About: SMPyBandits: an Open-Source Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python

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: Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox

Initial Announcement on mloss.org.

About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition

1. The MPCA paper is updated with a typo (the MAD measure in Table II) corrected.

2. Tensor toolbox version 2.1 is included for convenience.

3. Full code on gait recognition is included for verification and comparison.

About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms.

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.

About: Implementation of the multi-assignment clustering method for Boolean vectors.

new bib added

About: Multicore/distributed large scale machine learning framework.

Update version.

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.

• New and improved HDDM model with the following changes:
  • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
  • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
  • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
  • The old model has been renamed to HDDMTransformed.
  • HDDMRegression and HDDMStimCoding are also using this model.
• HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
• Improved online documentation at http://ski.clps.brown.edu/hddm_docs
• A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
• Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
• Maximum likelihood optimization.
• Many bugfixes and better test coverage.
• hddm_fit.py command line utility is depracated.

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: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules.

Moved from CRAN to Bioconductor. Improved handling of molecules, visualization and examples.

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.