Abstract
In this study, glandular tumor images are classified automatically by the support vector machine (SVM) in order to make up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. The fault of Mahalanobis' generalized distance is the problem, that is to say, the Curse of Dimensionality. To avoid this problem, we used the support vector machine (SVM) as the discriminant analysis, used the prostate images as glandular tumor images ,and examined the effectiveness of this system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology |
| Pages | 3101-3104 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2008 Dec 1 |
| Event | SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan Duration: 2008 Aug 20 → 2008 Aug 22 |
Publication series
| Name | Proceedings of the SICE Annual Conference |
|---|
Other
| Other | SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology |
|---|---|
| Country | Japan |
| City | Tokyo |
| Period | 08/8/20 → 08/8/22 |
Keywords
- Discriminant analysis
- Support vector machine
- Texture analysis
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
Fingerprint Dive into the research topics of 'Discriminant method for severity of glandular tumor by support vector machine'. Together they form a unique fingerprint.
Cite this
Suzuki, A., & Tanaka, T. (2008). Discriminant method for severity of glandular tumor by support vector machine. In Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology (pp. 3101-3104). [4655197] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2008.4655197