Projects that are tagged with regression.
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Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.0

by hn - October 19, 2016, 10:15:05 CET [ Project Homepage BibTeX Download ] 36946 views, 8392 downloads, 4 subscriptions

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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.


A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include grid-based covariance approximations natively.

More generic sparse approximation using Power EP

  • unified treatment of FITC approximation, variational approaches VFE and hybrids

  • inducing input optimisation for all (compositions of) covariance functions dropping the previous limitation to a few standard examples

  • infFITC is now covered by the more generic infGaussLik function

Approximate covariance object unifying sparse approximations, grid-based approximations and exact covariance computations

  • implementation in cov/apx, cov/apxGrid, cov/apxSparse

  • generic infGaussLik unifies infExact, infFITC and infGrid

  • generic infLaplace unifies infLaplace, infFITC_Laplace and infGrid_Laplace

Hiearchical structure of covariance functions

  • clear hierachical compositional implementation

  • no more code duplication as present in covSEiso and covSEard pairs

  • two mother covariance functions

    • covDot for dot-product-based covariances and

    • covMaha for Mahalanobis-distance-based covariances

  • a variety of modifiers: eye, iso, ard, proj, fact, vlen

  • more flexibility as more variants are available and possible

  • all covariance functions offer derivatives w.r.t. inputs

Faster derivative computations for mean and cov functions

  • switched from partial derivatives to directional derivatives

  • simpler and more concise interface of mean and cov functions

  • much faster marginal likelihood derivative computations

  • simpler and more compact code

New mean functions

  • new mean/meanWSPC (Weighted Sum of Projected Cosines or Random Kitchen Sink features) following a suggestion by William Herlands

  • new mean/meanWarp for constructing a new mean from an existing one by means of a warping function adapted from William Herlands

New optimizer

  • added a new minimize_minfunc, contributed by Truong X. Nghiem

New GLM link function

  • added the twice logistic link function util/glm_invlink_logistic2

Smaller fixes

  • two-fold speedup of util/elsympol used by covADD by Truong X. Nghiem

  • bugfix in util/logphi as reported by John Darby

Logo JMLR dlib ml 19.2

by davis685 - October 11, 2016, 01:54:09 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 156969 views, 25285 downloads, 5 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.


This release adds a number of new features, most important of which is a deep convolutional neural network version of the max-margin object detection algorithm. This tool makes it very easy to create high quality object detectors. See for an introduction.

Logo RLScore 0.7

by aatapa - September 20, 2016, 09:51:25 CET [ Project Homepage BibTeX Download ] 667 views, 104 downloads, 3 subscriptions

About: RLScore - regularized least-squares machine learning algorithms package


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Logo KeLP 2.1.0

by kelpadmin - August 11, 2016, 10:40:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11268 views, 2554 downloads, 3 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.


In addition to minor bug fixes, this release includes:

  • a flexible system to manipulate example-pairs
  • new manipulators for performing tree pruning
  • new examples for the usage of kelp

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.1.0!

Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 5740 views, 1314 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

  • Add Logistic Regression
  • Add support for sparse input in fast training of linear SVM
  • Better support for sparse vectors/matrices
  • Fix plot windows in IE
  • Minor bug fixes

Logo JMLR GPstuff 4.7

by avehtari - June 9, 2016, 17:45:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36793 views, 8871 downloads, 3 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.


2016-06-09 Version 4.7

Development and release branches available at

New features

  • Simple Bayesian Optimization demo


  • Improved use of PSIS
  • More options added to gp_monotonic
  • Monotonicity now works for additive covariance functions with selected variables
  • Possibility to use gpcf_squared.m-covariance function with derivative observations/monotonicity
  • Default behaviour made more robust by changing default jitter from 1e-9 to 1e-6
  • LA-LOO uses the cavity method as the default (see Vehtari et al (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. JMLR, accpeted for publication)
  • Selected variables -option works now better with monotonicity


  • small error in derivative observation computation fixed
  • several minor bug fixes

Logo AutoWEKA 2.0

by larsko - May 19, 2016, 19:58:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1453 views, 459 downloads, 3 subscriptions

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.


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Logo WEKA 3.9.0

by mhall - April 15, 2016, 06:35:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 62627 views, 9227 downloads, 5 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]


In core weka:

  • JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed
  • General efficiency improvements in core, filters and some classifiers
  • GaussianProcesses now handles instance weights
  • New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API
  • New Workbench GUI
  • GUI package manager now has a search facility
  • FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages:

  • Packages that were using JAMA are now using MTJ
  • New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra
  • New elasticNet package, courtesy of Nikhil Kinshore
  • New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka
  • New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error
  • New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error
  • New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning
  • discriminantAnalysis package now contains an implementation for LDA and QDA
  • New Knowledge Flow component implementations in various packages
  • newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual
  • RPlugin updated to latest version of MLR
  • scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries
  • Support for pluggable activation functions in the multiLayerPerceptrons package

Logo Java Statistical Analysis Tool 0.0.4

by EdwardRaff - March 5, 2016, 06:28:14 CET [ Project Homepage BibTeX Download ] 1254 views, 352 downloads, 2 subscriptions

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


Initial Announcement on

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 1612 views, 329 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.


Initial Announcement on

Logo YCML 0.2.2

by yconst - August 24, 2015, 20:28:45 CET [ Project Homepage BibTeX Download ] 1697 views, 384 downloads, 3 subscriptions

About: A Machine Learning framework for Objective-C and Swift (OS X / iOS)


Initial Announcement on

Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10411 views, 1824 downloads, 3 subscriptions

About: Hivemall is a scalable machine learning library running on Hive/Hadoop.

  • Supported Matrix Factorization
  • Added a support for TF-IDF computation
  • Supported AdaGrad/AdaDelta
  • Supported AdaGradRDA classification
  • Added normalization scheme

Logo pyGPs 1.3.2

by mn - January 17, 2015, 13:08:43 CET [ Project Homepage BibTeX Download ] 8272 views, 1906 downloads, 4 subscriptions

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


Changelog pyGPs v1.3.2

December 15th 2014

  • pyGPs added to pip
  • mathematical definitions of kernel functions available in documentation
  • more error message added

Logo The Statistical ToolKit 0.8.4

by joblion - December 5, 2014, 13:21:47 CET [ Project Homepage BibTeX Download ] 2910 views, 825 downloads, 2 subscriptions

About: STK++: A Statistical Toolkit Framework in C++


Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix

Logo linearizedGP 1.0

by dsteinberg - November 28, 2014, 07:02:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2343 views, 514 downloads, 1 subscription

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.


Initial Announcement on

Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 6713 views, 1935 downloads, 3 subscriptions

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

  • added ElasticNets as a regularization option
  • fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
  • added a couple of I/O optimizations

Logo Kernel Adaptive Filtering Toolbox 1.4

by steven2358 - May 26, 2014, 18:24:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8204 views, 1415 downloads, 1 subscription

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

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

Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 29248 views, 6381 downloads, 2 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.


New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)

Logo Orange 2.6

by janez - February 14, 2013, 18:15:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17801 views, 3455 downloads, 1 subscription

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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]


The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL.

Changed the BibTeX reference to the paper recently published in JMLR MLOSS.

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.


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