Projects that are tagged with regression.
Showing Items 1-20 of 47 on page 1 of 3: 1 2 3 Next

Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.1

by hn - November 27, 2017, 19:26:13 CET [ Project Homepage BibTeX Download ] 49518 views, 10927 downloads, 5 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.

Changes:

Logdet-estimation functionality for grid-based approximate covariances

  • Lanczos subspace estimation

  • Chebyshef polynomial expansion

More generic infEP functionality

  • dense computations and sparse approximations using the same code

  • covering KL inference as a special cas of EP

New infKL function contributed by Emtiyaz Khan and Wu Lin

  • Conjugate-Computation Variational Inference algorithm

  • much more scalable than previous versions

Time-series covariance functions on the positive real line

  • covW (i-times integrated) Wiener process covariance

  • covOU (i-times integrated) Ornstein-Uhlenbeck process covariance (contributed by Juan Pablo Carbajal)

  • covULL underdamped linear Langevin process covariance (contributed by Robert MacKay)

  • covFBM Fractional Brownian motion covariance

New covariance functions

  • covWarp implements k(w(x),w(z)) where w is a "warping" function

  • covMatern has been extended to also accept non-integer distance parameters


Logo MLweb 1.1

by lauerfab - November 10, 2017, 11:34:48 CET [ Project Homepage BibTeX Download ] 12667 views, 3021 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.

Changes:
  • Add gaxpy() and documentation on in-place operations
  • Add loo() function to Classifier and Regression models
  • New contributed toolbox for RNN
  • Minor fixes

Logo JMLR dlib ml 19.7

by davis685 - September 17, 2017, 15:10:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 205239 views, 31854 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.

Changes:

This release upgrades dlib's CNN+MMOD object detector to support creating multi-class detectors. It also includes significant speed improvements, allowing the detector to run at 98fps when executed on a NVIDIA 1080ti GPU. This release also adds a new 5 point face landmarking model that is over 10x smaller than the 68 point model, runs faster, and works with both HOG and CNN generated face detections. It is now the recommended landmarking model to use for face alignment.


Logo KeLP 2.2.1

by kelpadmin - August 7, 2017, 17:20:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19836 views, 4156 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.

Changes:

In addition to minor bug fixes, this release includes:

  • A new cache (FixSizeKernelCache) that can store a larger number of computations.

  • Evaluators for measuring the quality of Clustering algorithms.

Furthermore we also released the new module kelp-input-generator, that contains the facilities to parse text snippets and generate tree representations for 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 2.2.1!


About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

Changes:

Project is now fully scikit-learn compatible, implements 2 new predictor aggregation algorithms, more Jupyter notebooks and examples, and continuous integration for tests.


Logo Java Statistical Analysis Tool 0.0.7

by EdwardRaff - January 15, 2017, 22:21:50 CET [ Project Homepage BibTeX Download ] 3937 views, 968 downloads, 2 subscriptions

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

Changes:

See github release tab for change info


Logo WEKA 3.9.1

by mhall - December 19, 2016, 04:44:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 77118 views, 17204 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 [...]

Changes:

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 Tools for Regression and Classification 1.0.0

by matloff - October 29, 2016, 08:22:40 CET [ Project Homepage BibTeX Download ] 2262 views, 447 downloads, 3 subscriptions

About: Toolkit for parametric and nonparametric regression and classification.

Changes:

Initial Announcement on mloss.org.


Logo RLScore 0.7

by aatapa - September 20, 2016, 09:51:25 CET [ Project Homepage BibTeX Download ] 2001 views, 503 downloads, 3 subscriptions

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

Changes:

Initial Announcement on mloss.org.


Logo JMLR GPstuff 4.7

by avehtari - June 9, 2016, 17:45:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 49708 views, 12332 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.

Changes:

2016-06-09 Version 4.7

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

  • Simple Bayesian Optimization demo

Improvements

  • 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

Bugfixes

  • 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 ] 3211 views, 977 downloads, 3 subscriptions

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

Changes:

Initial Announcement on mloss.org.


Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 2611 views, 569 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.

Changes:

Initial Announcement on mloss.org.


Logo YCML 0.2.2

by yconst - August 24, 2015, 20:28:45 CET [ Project Homepage BibTeX Download ] 2621 views, 585 downloads, 3 subscriptions

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

Changes:

Initial Announcement on mloss.org.


Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13926 views, 2401 downloads, 3 subscriptions

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

Changes:
  • 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 ] 11465 views, 2563 downloads, 4 subscriptions

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

Changes:

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 ] 4289 views, 1065 downloads, 2 subscriptions

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

Changes:

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 ] 3423 views, 711 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 8881 views, 2487 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

Changes:
  • 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 MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 35505 views, 7461 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.

Changes:

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 ] 21857 views, 4019 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, [...]

Changes:

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.


Showing Items 1-20 of 47 on page 1 of 3: 1 2 3 Next