Optimisation on support vector machines

• Type: Article in International Conference Proceedings Book
• Date: 2000

Authors

Pedroso, JP (Author) Other; Murata, N (Author) Other

Conference proceedings International

Title: IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI

Pages: 399-404

IEEE/INNS/ENNS International Joint Conference on Neural Networks (IJCNN 2000)

COMO, ITALY, JUL 24-27, 2000

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-001-1N3

DOI: 10.1109/IJCNN.2000.859428

Abstract (EN): In this paper we deal with the optimisation problem involved in determining the maximal margin separation hyperplane in support vector machines. We consider three different formulations, based on L-2 norm distance (the standard case), L-1 norm, and L-infinity norm. We consider separation in the original space of the data (i.e., there are no kernel transformations). For any of these cases, we focus on the following problem: having the optimal solution for a given training data set, one is given a new training example. The purpose is to use the information about the solution of the problem without the additional example in order to speed up the new optimisation problem. We also consider the case of reoptimisation after removing an example from the data set. We report results obtained for some standard benchmark problems.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6