← Back to papers
2013 JMLR JMLR 2013

JKernelMachines: A Simple Framework for Kernel Machines

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). [abs] [ pdf ][ bib ] [ code ] © JMLR 2013. (edit, beta)

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
📈 Trend Setter — Kernel Methods
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

David Picard , Nicolas Thome , Matthieu Cord

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Kernel Methods Machine Learning > Bayesian & Probabilistic > Kernel Methods Mathematics & Optimization > Optimization > Kernel Methods Machine Learning > Optimization & Theory > Kernel Methods

Keywords

multiple kernel learning support vector machine kernel machine kernel methods
Download PDF

Related papers

Parallel Vector Field Embedding 2013
Semi-Supervised Learning Using Greedy Max-Cut 2013
Random Spanning Trees and the Prediction of Weighted Graphs 2013
Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems 2013
Training Energy-Based Models for Time-Series Imputation 2013