TY - JOUR
T1 - Classification of chromosome sequences with entropy kernel and LKPLS algorithm
AU - Liu, Zhenqiu
AU - Chen, Dechang
N1 - Funding Information:
D. Chen was supported by the National Science Foundation grant CCR-0311252. The authors wish to thank Dr. Jens Gregor for providing the preprocessed chromosome data. Note: The opinions expressed herein are those of the authors and do not necessarily represent those of the Uniformed Services University of the Health Sciences and the Department of Defense.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=27144550810&partnerID=8YFLogxK
U2 - 10.1007/11538059_57
DO - 10.1007/11538059_57
M3 - Conference article
AN - SCOPUS:27144550810
SN - 0302-9743
VL - 3644
SP - 543
EP - 551
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
IS - PART I
T2 - International Conference on Intelligent Computing, ICIC 2005
Y2 - 23 August 2005 through 26 August 2005
ER -