Task decomposition using geometric relation for min-max modular SVMs

Authors:
Kaian Wang;Hai Zhao;Baoliang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 3
Cited 5

Support-Vector Networks

Machine Learning
Task Decomposition Based on Class Relations: A Modular Neural Network Architecture for Pattern Classification

IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

IEEE Transactions on Neural Networks

Hierarchical fuzzy filter method for unsupervised feature selection

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Adaptive ensemble learning strategy using an assistant classifier for large-scale imbalanced patent categorization

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Gender recognition using a min-max modular support vector machine

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Gender recognition using a min-max modular support vector machine with equal clustering

ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Parallelized extreme learning machine ensemble based on min-max modular network

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The min-max modular support vector machine (M3-SVM) was proposed for dealing with large-scale pattern classification problems. M3-SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the geometric relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.