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JKernelMachines: A Simple Framework for Kernel Machines

David Picard 1, * Nicolas Thome 2 Matthieu Cord 2
* Corresponding author
1 MIDI - Multimedia Indexation and Data Integration
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : JKernelMachines is a Java library for learning with kernels. It is primarily designed to deal with custom kernels that are not easily found in standard libraries, such as kernels on structured data. These types of kernels are often used in computer vision or bioinformatics applications. We provide several kernels leading to state of the art classification performances in computer vision, as well as various kernels on sets. The main focus of the library is to be easily extended with new kernels. Standard SVM optimization algorithms are available, but also more sophisticated learning-based kernel combination methods such as Multiple Kernel Learning (MKL), and a recently published algorithm to learn powered products of similarities (Product Kernel Learning).
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Submitted on : Monday, June 10, 2013 - 7:51:55 AM
Last modification on : Monday, January 25, 2021 - 3:16:02 PM
Long-term archiving on: : Wednesday, September 11, 2013 - 4:10:37 AM


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  • HAL Id : hal-00832030, version 1


David Picard, Nicolas Thome, Matthieu Cord. JKernelMachines: A Simple Framework for Kernel Machines. Journal of Machine Learning Research, Microtome Publishing, 2013, 14, pp.1417-1421. ⟨hal-00832030⟩



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