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About: A fast and robust learning of Bayesian networks Changes:Initial Announcement on mloss.org.
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About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting). Changes:
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About: OLL is a library supporting several for online-learning algorithms, which provides C++ library, and stand-alone programs for learning, predicting. OLL is specialized for large-scale, but sparse, [...] Changes:Initial Announcement on mloss.org.
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About: ChemCpp is a C++ toolbox for chemoinformatics focusing on the computation of kernel functions between chemical compounds. Changes:Initial Announcement on mloss.org.
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About: General purpose Java Machine Learning library for classification, regression, and clustering. Changes:See github release tab for change info
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About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition Changes:
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About: Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to [...] Changes:Initial Announcement on mloss.org.
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About: Sequin is an open source sequence mining library written in C#. Changes:Sequin v1.1.0.0 released
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About: The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL and utilizes Intel Integrated Performance [...] Changes:Initial Announcement on mloss.org. |
About: Nested Effects Models (NEMs) are a class of directed graphical models originally introduced to analyze the effects of gene perturbation screens with high-dimensional phenotypes. In contrast to other [...] Changes:Initial Announcement on mloss.org.
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About: The SimpleSVM toolbox contains the svm solver of the same name. The current version includes C-SVM, HM-SVM and nu-SVM based on the regularization path. It will soon include OC-SVM, regularization [...] Changes:Initial Announcement on mloss.org.
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About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs). Changes:Initial Announcement on mloss.org.
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About: Torch5 provides a matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to a easy and fast scripting [...] Changes:Initial Announcement on mloss.org.
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About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, label-specific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving auto-association problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful low-dimensional mappings. Changes:Tue Jul 5 14:40:03 CEST 2011 - Bugfixes and cleanups
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About: We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver. Changes:Initial Announcement on mloss.org.
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About: Local alignment kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. Changes:Initial Announcement on mloss.org.
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About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation Changes:Release 0.7.0
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About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org
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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. Changes:improved testing, improved documentation, windows compatibility, more algorithms
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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'. Changes:Moved project to GitHub.
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