Constructing sparse kernel machines using attractors

Authors:
Daewon Lee;Kyu-Hwan Jung;Jaewook Lee
Affiliations:
Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, Korea
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 11
Cited 8

Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Exact simplification of support vector solutions

The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient method for simplifying support vector machines

ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems

Pattern Recognition
Clustering Based on Gaussian Processes

Neural Computation
Input space versus feature space in kernel-based methods

IEEE Transactions on Neural Networks
Equilibrium-Based Support Vector Machine for Semisupervised Classification

IEEE Transactions on Neural Networks

Improving memory-based collaborative filtering via similarity updating and prediction modulation

Information Sciences: an International Journal
Dynamic pattern denoising method using multi-basin system with kernels

Pattern Recognition
Transductive Bayesian regression via manifold learning of prior data structure

Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods

Expert Systems with Applications: An International Journal
Sequential manifold learning for efficient churn prediction

Expert Systems with Applications: An International Journal
Probabilistic generative ranking method based on multi-support vector domain description

Information Sciences: an International Journal
Inductive manifold learning using structured support vector machine

Pattern Recognition
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.