Autores
Cruz Torres Benjamín
Barrón Fernández Ricardo
Sossa Azuela Juan Humberto
Título Pattern Classification Based on Conformal Geometric Algebra and Optimization Techniques
Tipo Congreso
Sub-tipo SCOPUS
Descripción Lecture Notes in Computer Science; 7th Mexican International Conference on Artificial Intelligence
Resumen Conformal Geometric Algebra (CGA) is a high level language commonly used in mathematical, physics and engineering problems. At a top level, CGA is a free coordinate tool for designing and modeling geometric problems; at a low level CGA provides a new coordinate framework for numeric processing in problem solving. In this paper we show how to use quadratic programming and CGA for, given two sets p and q of points in ? n , construct an optimal separation sphere S such that, all points of p are contained inside of it, and all points of q are outside. To classify an unknown pattern x, an inner product must be applied between x and S. Some numerical and real examples to test the proposal are given.
Observaciones (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); MICAI 2008: Advances in Artificial Intelligence; Code 74372
Lugar Atizapán de Zaragoza
País Mexico
No. de páginas 273-283
Vol. / Cap. 5317
Inicio 2008-10-27
Fin 2008-10-31
ISBN/ISSN 978-354088635-8