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Logo JMLR GPstuff 4.4

by avehtari - April 15, 2014, 15:26:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7731 views, 2144 downloads, 1 subscription

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2014-04-11 Version 4.4

New features

  • Monotonicity constraint for the latent function.

    • Riihimäki and Vehtari (2010). Gaussian processes with monotonicity information. Journal of Machine Learning Research: Workshop and Conference Proceedings, 9:645-652.
  • State space implementation for GP inference (1D) using Kalman filtering.

    • For the following covariance functions: Squared-Exponential, Matérn-3/2 & 5/2, Exponential, Periodic, Constant
    • Särkkä, S., Solin, A., Hartikainen, J. (2013). Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing. IEEE Signal Processing Magazine, 30(4):51-61.
    • Simo Sarkka (2013). Bayesian filtering and smoothing. Cambridge University Press.
    • Solin, A. and Särkkä, S. (2014). Explicit link between periodic covariance functions and state space models. AISTATS 2014.

Improvements

  • GP_PLOT function for quick plotting of GP predictions
  • GP_IA now warns if it detects multimodal posterior distributions
  • much faster EP with log-Gaussian likelihood (numerical integrals -> analytical results)
  • faster WAIC with GP_IA array (numerical integrals -> analytical results)
  • New demos demonstrating new features etc.
    • demo_minimal, minimal demo for regression and classification
    • demo_kalman1, demo_kalman2
    • demo_monotonic, demo_monotonic2

Plus bug fixes


Logo Chordalysis 1.0

by fpetitjean - March 24, 2014, 01:22:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 223 views, 33 downloads, 1 subscription

About: Log-linear analysis for high-dimensional data

Changes:

Initial Announcement on mloss.org.


Logo ExtRESCAL 0.6

by nzhiltsov - March 21, 2014, 16:22:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1308 views, 270 downloads, 1 subscription

About: Scalable tensor factorization

Changes:
  • Make the extended algorigthm output fixed (by replacing random initialization)
  • Add handling of float values in the extended task
  • Add the util for matrix pseudo inversion
  • Switch to Apache License 2.0

Logo MOSIS 0.55

by claasahl - March 9, 2014, 17:35:40 CET [ BibTeX Download ] 1803 views, 572 downloads, 2 subscriptions

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

Changes:
  • 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.

Logo APCluster 1.3.3

by UBod - February 24, 2014, 09:38:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12996 views, 2425 downloads, 2 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • adapted dependencies and linking to Rcpp version 0.11.0
  • cleared up package dependencies

Logo The Choquet Kernel 1.00

by AliFall - February 11, 2014, 16:21:15 CET [ BibTeX BibTeX for corresponding Paper Download ] 394 views, 81 downloads, 1 subscription

About: The package computes the optimal parameters for the Choquet kernel

Changes:

Initial Announcement on mloss.org.


Logo Ordinal Choquistic Regression 1.00

by AliFall - January 30, 2014, 15:42:34 CET [ BibTeX BibTeX for corresponding Paper Download ] 494 views, 99 downloads, 1 subscription

About: "Ordinal Choquistic Regression" model using the maximum likelihood

Changes:

Initial Announcement on mloss.org.


Logo ELKI 0.6.0

by erich - January 10, 2014, 18:32:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7997 views, 1498 downloads, 3 subscriptions

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Changes:

Additions and Improvements from ELKI 0.5.5:

Algorithms

Clustering:

  • Hierarchical Clustering - the slower naive variants were added, and the code was refactored
  • Partition extraction from hierarchical clusterings - different linkage strategies (e.g. Ward)
  • Canopy pre-Clustering
  • Naive Mean-Shift Clustering
  • Affinity propagation clustering (both with distances and similarities / kernel functions)
  • K-means variations: Best-of-multiple-runs, bisecting k-means
  • New k-means initialization: farthest points, sample initialization
  • Cheng and Church Biclustering
  • P3C Subspace Clustering
  • One-dimensional clustering algorithm based on kernel density estimation

Outlier detection

  • COP - correlation outlier probabilities
  • LDF - a kernel density based LOF variant
  • Simplified LOF - a simpler version of LOF (not using reachability distance)
  • Simple Kernel Density LOF - a simple LOF using kernel density (more consistent than LDF)
  • Simple outlier ensemble algorithm
  • PINN - projection indexed nearest neighbors, via projected indexes.
  • ODIN - kNN graph based outlier detection
  • DWOF - Dynamic-Window Outlier Factor (contributed by Omar Yousry)
  • ABOD refactored, into ABOD, FastABOD and LBABOD

Distances

  • Geodetic distances now support different world models (WGS84 etc.) and are subtantially faster.
  • Levenshtein distances for processing strings, e.g. for analyzing phonemes (contributed code, see "Word segmentation through cross-lingual word-to-phoneme alignment", SLT2013, Stahlberg et al.)
  • Bray-Curtis, Clark, Kulczynski1 and Lorentzian distances with R-tree indexing support
  • Histogram matching distances
  • Probabilistic divergence distances (Jeffrey, Jensen-Shannon, Chi2, Kullback-Leibler)
  • Kulczynski2 similarity
  • Kernel similarity code has been refactored, and additional kernel functions have been added

Database Layer and Data Types

Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and p-stable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogram-based jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA)

Index Layer

  • Another speed up in R-trees
  • Refactoring of M- and R-trees: Support for different strategies in M-tree New strategies for M-tree splits Speedups in M-tree
  • New index structure: in-memory k-d-tree
  • New index structure: in-memory Locality Sensitive Hashing (LSH)
  • New index structure: approximate projected indexes, such as PINN
  • Index support for geodetic data - (Details: Geodetic Distance Queries on R-Trees for Indexing Geographic Data, SSTD13)
  • Sampled k nearest neighbors: reference KDD13 "Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles"
  • Cached (precomputed) k-nearest neighbors to share across multiple runs
  • Benchmarking "algorithms" for indexes

Mathematics and Statistics

  • Many new distributions have been added, now 28 different distributions are supported
  • Additional estimation methods (using advanced statistics such as L-Moments), now 44 estimators are available
  • Trimming and Winsorizing
  • Automatic best-fit distribution estimation
  • Preprocessor using these distributions for rescaling data sets
  • API changes related to the new distributions support
  • More kernel density functions
  • RANSAC covariance matrix builder (unfortunately rather slow)

Visualization

  • 3D projected coordinates (Details: Interactive Data Mining with 3D-Parallel-Coordinate-Trees, SIGMOD2013)
  • Convex hulls now also include nested hierarchical clusters

Other

  • Parser speedups
  • Sparse vector bug fixes and improvements
  • Various bug fixes
  • PCA, MDS and LDA filters
  • Text output was slightly improved (but still needs to be redesigned from scratch - please contribute!)
  • Refactoring of hierarchy classes
  • New heap classes and infrastructure enhancements
  • Classes can have aliases, e.g. "l2" for euclidean distance.
  • Some error messages were made more informative.
  • Benchmarking classes, also for approximate nearest neighbor search.

Logo JMLR MLPACK 1.0.8

by rcurtin - January 7, 2014, 05:47:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27523 views, 5513 downloads, 5 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Memory leak in NeighborSearch index-mapping code fixed.
  • GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate().
  • Logistic regression implementation added in methods/logistic_regression.
  • Version information is now obtainable via mlpack::util::GetVersion() or the _MLPACKVERSION_MAJOR, _MLPACKVERSION_MINOR, and _MLPACKVERSION_PATCH macros.
  • Fix typos in allkfn and allkrann output.

Logo minFunc 2012

by markSchmidt - December 18, 2013, 01:07:07 CET [ Project Homepage BibTeX Download ] 550 views, 88 downloads, 1 subscription

About: minFunc is a Matlab function for unconstrained optimization of differentiable real-valued multivariate functions using line-search methods. It uses an interface very similar to the Matlab Optimization Toolbox function fminunc, and can be called as a replacement for this function. On many problems, minFunc requires fewer function evaluations to converge than fminunc (or minimize.m). Further it can optimize problems with a much larger number of variables (fminunc is restricted to several thousand variables), and uses a line search that is robust to several common function pathologies.

Changes:

Initial Announcement on mloss.org.


Logo LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5485 views, 1336 downloads, 2 subscriptions

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.

Changes:

In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.


About: RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in Reinforcement Learning. It is an optimized library for robotic applications that operates under fast duty cycles (e.g., ≤ 30 ms). RLLib has been tested and evaluated on Robocup 3D Soccer Simulation agents and physical NAO V4 humanoid robots to learn behaviors and represent learnable knowledge.

Changes:

Current release version is v1.6.


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.4

by hn - November 11, 2013, 14:46:52 CET [ Project Homepage BibTeX Download ] 14644 views, 3873 downloads, 3 subscriptions

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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:
  • derivatives w.r.t. inducing points xu in infFITC, infFITC_Laplace, infFITC_EP so that one can treat the inducing points either as fixed given quantities or as additional hyperparameters
  • new GLM likelihood likExp for inter-arrival time modeling
  • new GLM likelihood likWeibull for extremal value regression
  • new GLM likelihood likGumbel for extremal value regression
  • new mean function meanPoly depending polynomially on the data
  • infExact can deal safely with the zero noise variance limit
  • support of GP warping through the new likelihood function likGaussWarp

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

Changes:

added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes

generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used


Logo Kernel Adaptive Filtering Toolbox 1.3

by steven2358 - October 21, 2013, 18:15:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1572 views, 246 downloads, 1 subscription

About: A Matlab benchmarking toolbox for online and adaptive regression with kernels.

Changes:

Inclusion of Gaussian process based parameter estimation, and several new regression algorithms.


Logo FABIA 2.8.0

by hochreit - October 18, 2013, 10:14:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7719 views, 1605 downloads, 1 subscription

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About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

Changes:

CHANGES IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

  • spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
  • fabia for non-negative factorization (parameter: non_negative);
  • changed to C and removed dependencies to Rcpp;
  • improved update for lambda (alpha should be smaller, e.g. 0.03);
  • introduced maximal number of row elements (lL);
  • introduced cycle bL when upper bounds nL or lL are effective;
  • reduced computational complexity;
  • bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

  • New option nL: maximal number of biclusters per row element;
  • Sort biclusters according to information content;
  • Improved and extended preprocessing;
  • Update to R2.13

Logo MLlib 0.8

by atalwalkar - October 10, 2013, 00:56:25 CET [ Project Homepage BibTeX Download ] 1021 views, 211 downloads, 1 subscription

About: MLlib provides a distributed machine learning (ML) library to address the growing need for scalable ML. MLlib is developed in Spark (http://spark.incubator.apache.org/), a cluster computing system designed for iterative computation. Moreover, it is a component of a larger system called MLbase (www.mlbase.org) that aims to provide user-friendly distributed ML functionality both for ML researchers and domain experts. MLlib currently consists of scalable implementations of algorithms for classification, regression, collaborative filtering and clustering.

Changes:

Initial Announcement on mloss.org.


Logo RLPy 1.2

by bobklein2 - October 9, 2013, 03:33:43 CET [ Project Homepage BibTeX Download ] 926 views, 179 downloads, 1 subscription

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:

Initial Announcement on mloss.org.


Logo pyGPs 1.1

by mn - October 8, 2013, 12:35:28 CET [ Project Homepage BibTeX Download ] 919 views, 240 downloads, 2 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

pyGPs v1.1 is released. It replaces pyGP_OO and contains substaintal updates in functionality and documentation. pyGP_PR v1.1 is released with substantial documentation updates and renamed (FN -> PR).


Logo BayesPy 0.1

by jluttine - September 25, 2013, 16:10:58 CET [ Project Homepage BibTeX Download ] 557 views, 188 downloads, 1 subscription

About: Variational Bayesian inference tools for Python

Changes:

Initial Announcement on mloss.org.


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