Classification of chromosome sequences with entropy kernel and LKPLS algorithm

Authors:
Zhenqiu Liu;Dechang Chen
Affiliations:
Department of Statistics, The Ohio State University, Columbus, OH;Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 5
Cited 0

Information retrieval: data structures and algorithms

Information retrieval: data structures and algorithms
Exploiting generative models in discriminative classifiers

Proceedings of the 1998 conference on Advances in neural information processing systems II
A disagreement count scheme for inference of constrained Markov networks

ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Mismatch string kernels for discriminative protein classification

Bioinformatics
Some inequalities for information divergence and related measures of discrimination

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Kernel methods such as support vector machines have been used extensively for various classification tasks. In this paper, we describe an entropy based string kernel and a novel logistic kernel partial least square algorithm for classification of sequential data. Our experiments with a human chromosome dataset show that the new kernel can be computed efficiently and the algorithm leads to a high accuracy especially for the unbalanced training data.