𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Improving support vector machine classifiers by modifying kernel functions

✍ Scribed by S. Amari; S. Wu

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
624 KB
Volume
12
Category
Article
ISSN
0893-6080

No coin nor oath required. For personal study only.

✦ Synopsis

We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal mapping, such that the separability between classes is increased. Examples are given specifically for modifying Gaussian Radial Basis Function kernels. Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting our idea.

πŸ“œ SIMILAR VOLUMES

Development of the cubic least squares m
Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound
✍ N. Piliouras; I. Kalatzis; N. Dimitropoulos; D. Cavouras πŸ“‚ Article πŸ“… 2004 πŸ› Elsevier Science 🌐 English βš– 286 KB

An efficient classification algorithm is proposed for characterizing breast lesions. The algorithm is based on the cubic least squares mapping and the linear-kernel support vector machine (SVM LSM ) classifier. Ultrasound images of 154 confirmed lesions (59 benign and 52 malignant solid masses, 7 si