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Logo JMLR MLPACK 1.0.10

by rcurtin - August 29, 2014, 21:26:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 31277 views, 6272 downloads, 6 subscriptions

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

Changes:
  • Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are nwo restored to approximately 1.0.8 speeds, with significant improvement for the cover tree (#365).
  • Detect dependencies correctly when ARMA_USE_WRAPPER is not defined (i.e. libarmadillo.so does not exist).
  • Bugfix for compilation under Visual Studio (#366).

Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 1439 views, 322 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:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo BayesPy 0.2

by jluttine - August 14, 2014, 17:24:22 CET [ Project Homepage BibTeX Download ] 1172 views, 344 downloads, 2 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • added all common distributions: Poisson, beta, multinomial, Bernoulli, categorical, etc

  • added Gaussian arrays (not just scalars or vectors)

  • added Gaussian Markov chains with time-varying or swithing dynamics

  • added discrete Markov chains (enabling hidden Markov models)

  • added deterministic gating node

  • added deterministic general sum-product node

  • added parameter expansion

  • added new plotting functions: pdf, Hinton diagram

  • added monitoring of posterior distributions during iteration

  • improved documentation


Logo Toeblitz Toolkit for Fast Toeplitz Matrix Operations 1.03

by cunningham - August 13, 2014, 02:21:36 CET [ BibTeX Download ] 1959 views, 503 downloads, 2 subscriptions

About: Toeblitz is a MATLAB/Octave package for operations on positive definite Toeplitz matrices. It can solve Toeplitz systems Tx = b in O(n*log(n)) time and O(n) memory, compute matrix inverses T^(-1) (with free log determinant) in O(n^2) time and memory, compute log determinants (without inverses) in O(n^2) time and O(n) memory, and compute traces of products A*T for any matrix A, in minimal O(n^2) time and memory.

Changes:

Adding a write-up in written/toeblitz.pdf describing the package.


Logo JMLR GPstuff 4.5

by avehtari - July 22, 2014, 14:03:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12060 views, 3158 downloads, 2 subscriptions

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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:

2014-07-22 Version 4.5

New features

  • Input dependent noise and signal variance.

    • Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Expectation Propagation for Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication. Preprint http://arxiv.org/abs/1404.5443
  • Sparse stochastic variational inference model.

    • Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes for big data. arXiv preprint http://arxiv.org/abs/1309.6835.
  • Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the posterior predictive distribution of the latent variable.

    • Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, 57(6):1317-1339.

    • Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution and Asymmetric Principal Components Analysis. Communications in Statistics - Theory and Methods, 35(6):1123-1140.

Improvements

  • New unit test environment using the Matlab built-in test framework (the old Xunit package is still also supported).
  • Precomputed demo results (including the figures) are now available in the folder tests/realValues.
  • New demos demonstrating new features etc.
    • demo_epinf, demonstrating the input dependent noise and signal variance model
    • demo_svi_regression, demo_svi_classification
    • demo_modelcomparison2, demo_survival_comparison

Several minor bugfixes


Logo pyGPs 1.2

by mn - July 17, 2014, 10:28:55 CET [ Project Homepage BibTeX Download ] 1731 views, 422 downloads, 2 subscriptions

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

Changes:

Changelog pyGPs v1.2

June 30th 2014

structural updates:

  • input target now can either be in 2-d array with size (n,1) or in 1-d array with size (n,)
  • setup.py updated
  • "import pyGPs" instead of "from pyGPs.Core import gp"
  • rename ".train()" to ".optimize()"
  • rename "Graph-stuff" to "graphExtension"
  • rename kernelOnGraph to "nodeKernels" and graphKernel to "graphKernels"
  • redundancy removed for model.setData(x,y)
  • rewrite "mean.proceed()" to "getMean()" and "getDerMatrix()"
  • rewrite "cov.proceed()" to "getCovMatrix()" and "getDerMatrix()"
  • rename cov.LIN to cov.Linear (to be consistent with mean.Linear)
  • rename module "valid" to "validation"
  • add graph dataset Mutag in python file. (.npz and .mat)
  • add graphUtil.nomalizeKernel()
  • fix number of iteration problem in graphKernels "PropagationKernel"
  • add unit testing for covariance, mean functions

bug fixes:

  • derivatives for cov.LINard
  • derivative of the scalar for cov.covScale
  • demo_GPR_FITC.py missing pyGPs.mean

July 8th 2014

structural updates:

  • add hyperparameter(signal variance s2) for linear covariance
  • add unit testing for inference,likelihood functions as well as models
  • NOT show(print) "maximum number of sweep warning in inference EP" any more
  • documentation updated

bug fixes:

  • typos in lik.Laplace
  • derivative in lik.Laplace

July 14th 2014

documentation updates:

  • online docs updated
  • API file updated

structural updates:

  • made private for methods that users don't need to call

Logo Crino 1.0.0

by jlerouge - July 16, 2014, 17:54:55 CET [ Project Homepage BibTeX Download ] 416 views, 77 downloads, 2 subscriptions

About: Crino: a neural-network library based on Theano

Changes:

1.0.0 (7 july 2014) : - Initial release of crino - Implements a torch-like library to build artificial neural networks (ANN) - Provides standard implementations for : * auto-encoders * multi-layer perceptrons (MLP) * deep neural networks (DNN) * input output deep architecture (IODA) - Provides a batch-gradient backpropagation algorithm, with adaptative learning rate


Logo APCluster 1.3.5

by UBod - June 30, 2014, 08:32:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15448 views, 2860 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:
  • memory access fixes in C++ code called from apclusterL()
  • minor updates of vignette

Logo Kernel Adaptive Filtering Toolbox 1.4

by steven2358 - May 26, 2014, 18:24:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2756 views, 426 downloads, 1 subscription

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

Changes:
  • Improvements and demo script for profiler
  • Initial version of documentation
  • Several new algorithms

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 and embedded devices that operates under fast duty cycles (e.g., < 30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, physical NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behaviors, and represent learnable knowledge. The implementation of the RLLib library is inspired by the RLPark API, which is a library of temporal-difference learning algorithms written in Java.

Changes:

Current release version is v2.0.


Logo Chordalysis 1.0

by fpetitjean - March 24, 2014, 01:22:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 661 views, 144 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 ] 1922 views, 394 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 ] 2435 views, 799 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 The Choquet Kernel 1.00

by AliFall - February 11, 2014, 16:21:15 CET [ BibTeX BibTeX for corresponding Paper Download ] 691 views, 183 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 ] 851 views, 198 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 ] 9542 views, 1754 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 minFunc 2012

by markSchmidt - December 18, 2013, 01:07:07 CET [ Project Homepage BibTeX Download ] 892 views, 183 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 ] 6907 views, 2087 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.


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.4

by hn - November 11, 2013, 14:46:52 CET [ Project Homepage BibTeX Download ] 18243 views, 4356 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


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