Abstract
We show in this paper, that a simple power series model with appropriate learning formulation, can be used for effective pattern classification. Essentially, an error counting cost function is adopted. Through a linear parametric power series model and a quadratic approximation to the error cost, a deterministic solution is derived. This solution is seen to relate to a class-specific setting of the more generic weighted least-squares. A tuning mechanism is thus incorporated for robust applications. Our empirical evaluations show effectiveness of the classifier.
| Original language | English |
|---|---|
| Title of host publication | ICIEA 2007 |
| Subtitle of host publication | 2007 Second IEEE Conference on Industrial Electronics and Applications |
| Pages | 641-646 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 Oct 18 |
| Event | 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007 - Harbin, China Duration: 2007 May 23 → 2007 May 25 |
Other
| Other | 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007 |
|---|---|
| Country | China |
| City | Harbin |
| Period | 07/5/23 → 07/5/25 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
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Simple power series for pattern classification. / Toh, Kar Ann.
ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications. 2007. p. 641-646 4318486.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Simple power series for pattern classification
AU - Toh, Kar Ann
PY - 2007/10/18
Y1 - 2007/10/18
N2 - We show in this paper, that a simple power series model with appropriate learning formulation, can be used for effective pattern classification. Essentially, an error counting cost function is adopted. Through a linear parametric power series model and a quadratic approximation to the error cost, a deterministic solution is derived. This solution is seen to relate to a class-specific setting of the more generic weighted least-squares. A tuning mechanism is thus incorporated for robust applications. Our empirical evaluations show effectiveness of the classifier.
AB - We show in this paper, that a simple power series model with appropriate learning formulation, can be used for effective pattern classification. Essentially, an error counting cost function is adopted. Through a linear parametric power series model and a quadratic approximation to the error cost, a deterministic solution is derived. This solution is seen to relate to a class-specific setting of the more generic weighted least-squares. A tuning mechanism is thus incorporated for robust applications. Our empirical evaluations show effectiveness of the classifier.
UR - http://www.scopus.com/inward/record.url?scp=35248812568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35248812568&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2007.4318486
DO - 10.1109/ICIEA.2007.4318486
M3 - Conference contribution
AN - SCOPUS:35248812568
SN - 1424407370
SN - 9781424407378
SP - 641
EP - 646
BT - ICIEA 2007
ER -