𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Design efficient support vector machine for fast classification

✍ Scribed by Yiqiang Zhan; Dinggang Shen


Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
184 KB
Volume
38
Category
Article
ISSN
0031-3203

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Texture classification using the support
✍ Shutao Li; James T. Kwok; Hailong Zhu; Yaonan Wang πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 515 KB

In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classiΓΏcation, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the pro

Efficient performance estimate for one-c
✍ Quang-Anh Tran; Xing Li; Haixin Duan πŸ“‚ Article πŸ“… 2005 πŸ› Elsevier Science 🌐 English βš– 188 KB

This letter proposes and analyzes a method (naq-estimate) to estimate the generalization performance of one-class support vector machine (SVM) for novelty detection. The method is an extended version of the na-estimate method, which is used to estimate the generalization performance of standard SVM

State classification of CBN grinding wit
✍ Neng-Hsin Chiu; Yu-Yang Guao πŸ“‚ Article πŸ“… 2008 πŸ› Elsevier Science 🌐 English βš– 831 KB

When grinding high-strength ferrous alloy with CBN wheel, attention is often paid to the variation of wheel surface condition to ensure work surface quality, since wheel sharpness is directly related to the ground surface. The on-line wheel condition can be obtained via process monitoring, which int