Projects that are tagged with kernels.

Logo KeLP 2.0.0

by kelpadmin - November 26, 2015, 16:14:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3925 views, 983 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 classifiers without writing a single line of code.


This is a major release that includes brand new features as well as a renewed architecture of the entire project.

Now KeLP is organized in four maven projects:

  • kelp-core: it contains the infrastructure of abstract classes and interfaces to work with KeLP. Furthermore, some implementations of algorithms, kernels and representations are included, to provide a base operative environment.

  • kelp-additional-kernels: it contains several kernel functions that extend the set of kernels made available in the kelp-core project. Moreover, this project implements the specific representations required to enable the application of such kernels. In this project the following kernel functions are considered: Sequence kernels, Tree kernels and Graphs kernels.

  • kelp-additional-algorithms: it contains several learning algorithms extending the set of algorithms provided in the kelp-core project, e.g. the C-Support Vector Machine or ν-Support Vector Machine learning algorithms. In particular, advanced learning algorithms for classification and regression can be found in this package. The algorithms are grouped in: 1) Batch Learning, where the complete training dataset is supposed to be entirely available during the learning phase; 2) Online Learning, where individual examples are exploited one at a time to incrementally acquire the model.

  • kelp-full: this is the complete package of KeLP. It aggregates the previous modules in one jar. It contains also a set of fully functioning examples showing how to implement a learning system with KeLP. Batch learning algorithm as well as Online Learning algorithms usage is shown here. Different examples cover the usage of standard kernel, Tree Kernels and Sequence Kernel, with caching mechanisms.

Furthermore this new release includes:

  • CsvDatasetReader: it allows to read files in CSV format

  • DCDLearningAlgorithm: it is the implementation of the Dual Coordinate Descent learning algorithm

  • methods for checking the consistency of a dataset.

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 2.0.0!

Logo KeBABS 1.4.1

by UBod - November 3, 2015, 11:33:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8185 views, 1466 downloads, 3 subscriptions

About: Kernel-Based Analysis of Biological Sequences

  • new method to compute prediction profiles from models trained with mixture kernels
  • correction for position specific kernel with offsets
  • corrections for prediction profile of motif kernel
  • additional hint on help page of kbsvm

Logo JMLR JKernelMachines 2.5

by dpicard - December 11, 2014, 17:51:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22210 views, 5106 downloads, 4 subscriptions

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About: machine learning library in java for easy development of new kernels


Version 2.5

  • New active learning algorithms
  • Better threading management
  • New multiclass SVM algorithm based on SDCA
  • Handle class balancing in cross-validation
  • Optional support of EJML switch to version 0.26
  • Various bugfixes and improvements

Logo APCluster 1.4.1

by UBod - December 10, 2014, 12:58:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25931 views, 4606 downloads, 3 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.

  • fixes in C++ code of sparse affinity propagation

Logo Rchemcpp 1.99.0

by klambaue - September 10, 2013, 09:10:13 CET [ Project Homepage BibTeX Download ] 4511 views, 1165 downloads, 1 subscription

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About: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules.


Moved from CRAN to Bioconductor. Improved handling of molecules, visualization and examples.

Logo OpenKernel library 0.1

by allauzen - April 23, 2010, 05:25:20 CET [ Project Homepage BibTeX Download ] 10725 views, 1391 downloads, 1 subscription

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About: OpenKernel is a library for creating, combining, learning and using kernels for machine learning applications.


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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 19600 views, 8075 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]


This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

Logo LASVM 1.1

by leonbottou - August 3, 2009, 15:50:30 CET [ Project Homepage BibTeX Download ] 10039 views, 1857 downloads, 0 subscriptions

About: Reference implementation of the LASVM online and active SVM algorithms as described in the JMLR paper. The interesting bit is a small C library that implements the LASVM process and reprocess [...]


Minor bug fix

Logo Easysvm 0.3

by gxr - June 25, 2009, 18:33:04 CET [ Project Homepage BibTeX Download ] 9220 views, 1829 downloads, 1 subscription

About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way.


Fixes for shogun 0.7.3.

Logo RapidMiner 4.0

by ingomierswa - November 16, 2007, 02:31:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17702 views, 3159 downloads, 0 comments, 0 subscriptions

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About: RapidMiner (formerly YALE) is one of the most widely used open-source data mining suites and software solutions due to its leading-edge technologies and its functional range. Applications of [...]


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