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MLPACK is the first comprehensive scalable machine learning library.
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A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.
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SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based [...]
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kernlab provides kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab [...]
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Java package implementing a kernel for (molecular) graphs based on iterative graph similarity and optimal assignments.
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LaRank is an online solver for multiclass Support Vector Machines.
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Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox
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SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, One-Class, SV Regression, AUC-SVM and Rankboost, 1-norm SVM, Regularization Networks, Kernel Basis Pursuit [...]
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PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods.
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We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver.
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BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods
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Code for automatically selecting the kernel parameters of an SVM. It is based on a gradient descent minimization of either the radius/margin bound, the leave-one-out error, a validation error or the [...]
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This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (PGPDT) for distributed memory, strictly coupled multiprocessor [...]
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ChemCpp is a C++ toolbox for chemoinformatics focusing on the computation of kernel functions between chemical compounds.
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Very simple code for training SVMs in the primal. Works particularly well on sparse linear problems. In the non-linear case the entire kernel matrix needs to be computed, so for large problems it is [...]
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The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...]
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