Beschreibung
This book discusses the kernel function selection problem of SVM and its solution towards increasing the efficiency of SVM classifiers. It demonstrates that by combining the good characteristics of two kernel functions, it is possible to have a generalized kernel. It also discusses that the SVM can be made selection parameter invariant, if it is possible to avoid the kernel function for the classification of unseen samples during the classification phase. In a nutshell, this book gives a new kernel function, RBPK, and a classifier, EuDiC, in context of SVM.
Autorenporträt
Hetal Bhavsar did her master's in CSE from DD University, PhD from CHARUSAT University, India. She has 18+ years of teaching experience and currently working as an Assistant Professor at The M. S. University of Baroda. She has published 20+ papers in many journals of repute and at int. conferences. Her area of research are Data Mining and Big Data.
Herstellerkennzeichnung:
BoD - Books on Demand
In de Tarpen 42
22848 Norderstedt
DE
E-Mail: info@bod.de
