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.
Original language | English (US) |
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Pages (from-to) | 543-551 |
Number of pages | 9 |
Journal | Lecture Notes in Computer Science |
Volume | 3644 |
Issue number | PART I |
DOIs | |
State | Published - 2005 |
Event | International Conference on Intelligent Computing, ICIC 2005 - Hefei, China Duration: Aug 23 2005 → Aug 26 2005 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- General Computer Science