Title :
Large margin strategies in machine learning
Author :
Cristianini, Nello
Author_Institution :
Bristol Univ., UK
Abstract :
Controlling the capacity of a learning system in a way that does not depend on the dimensionality of the hypothesis space provides the key for effectively using large neural networks and decision trees, ensemble methods and kernel-induced feature spaces. This extended abstract will provide an overview of recent work in this direction, based on the concepts of margin and margin distribution
Keywords :
decision trees; learning (artificial intelligence); neural nets; decision trees; ensemble methods; hypothesis space; kernel-induced feature spaces; large neural networks; learning system capacity; machine learning; margin distribution; Bayesian methods; Control systems; Decision trees; Intelligent networks; Kernel; Learning systems; Machine learning; Neural networks; Size control; Virtual colonoscopy;
Conference_Titel :
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location :
Geneva
Print_ISBN :
0-7803-5482-6
DOI :
10.1109/ISCAS.2000.856438
