Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; 13(18):549−553, 2012. Abstract The MULTIBOOST package provides a fast C++ implementation of multi-class/multi-label/multi-task boosting algorithms. It is based on ADABOOST.MH but it also implements popular cascade classifiers and FILTERBOOST. The package contains common multi-class base learners (stumps, t

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News 09.03.2016 MDP 3.5 released! This is a bug-fix release Note that from this release MDP is in maintenance mode. 13 years after its first public release, MDP has reached full maturity and no new features are planned in the future. If you plan to do serious machine learning in Python, use sklearn. Note though that some algorithms, notably SFA and Growing Neural Gas, are only available in MDP. We

MSVMpack: a Multi-class Support Vector Machine package by F. Lauer and Y. Guermeur, members of the ABC research team at LORIA MSVMpack is an open source package dedicated to multi-class support vector machines: SVMs which can handle classification problems with more than two classes without relying on decomposition methods. The aim is to provide a unified framework and implementation for all the d

Michael Gashler; 12(69):2383−2387, 2011. Abstract We present a breadth-oriented collection of cross-platform command-line tools for researchers in machine learning called Waffles. The Waffles tools are designed to offer a broad spectrum of functionality in a manner that is friendly for scripted automation. All functionality is also available in a C++ class library. Waffles is available under the G

Index of /Projects/DLLearner NameLast modifiedSizeDescription Parent Directory  - Architecture/2015-01-29 08:12 - Carcinogenesis/2015-01-29 08:25 - LearningExamples/2015-01-29 08:13 - Overview/2015-02-03 07:15 - PatternEnrichment/2015-01-29 08:12 - Php_client/2015-02-03 07:15 - ProtegePlugin/2015-01-29 08:13 - Screenshots/2015-01-29 08:18 - Apache/2.4.7 (Ubuntu) Server at dl-learner.org Port 80

Documentation for GPML Matlab Code version 4.2 1) What? The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexible framework for specifying GPs. Other GP packages can be found here. The code is written by Carl Edward Rasmussen

Shogun - A Large Scale Machine Learning Toolbox This is the official homepage of the SHOGUN machine learning toolbox. The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS [21], Liblinear [20], LibSVM [2], S

Free AI Photo Editor Powerful AI toolset for all your creative photo and design editing needs. Supercharge your creativity and let your imagination run wild. Start Creating Our AI Powered Tools These tools are specifically tuned for quality and are running most advanced AI models. New tools are being added constantly

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued

Alpenglow 1.0.6 About: A recommender systems research framework aimed at modeling non-stationary environments. Changes: Initial Announcement on mloss.org.

The Library Algorithms API Wrappers Bayesian Nets Compression Containers Imaging Machine Learning Metaprogramming Miscellaneous Network Parsing Help/Info Examples Home How to compile How to contribute Index Introduction License Current Release Change Log Release Notes Version: 17.21 Sourceforge Download Files Forums Known Users Summary This page documents all the machine learning algorithms presen