DocumentCode
3435127
Title
Statistical learning and VC theory
Author
Bartlett, Peter
fYear
2001
fDate
2001
Abstract
The article applies statistical learning theory to the supervised learning problem. Pattern recognition is covered, including Vapnik-Chervonenkis (VC) theory and the implications for support vector machines (SVMs), neural networks and decision trees. Real predictions are given for scale-sensitive dimensions. The article concludes by analysing large margin classification
Keywords
decision trees; learning (artificial intelligence); learning automata; neural nets; pattern recognition; VC theory; decision tree; large margin classification; neural network; pattern recognition; statistical learning; supervised learning; support vector machine; Classification tree analysis; Decision trees; Joining processes; Mobile handsets; Neural networks; Pattern recognition; Predictive models; Statistical learning; Supervised learning; Virtual colonoscopy;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2001. Tutorial Guide: ISCAS 2001. The IEEE International Symposium on
Conference_Location
Sydney, NSW
Print_ISBN
0-7803-7113-5
Type
conf
DOI
10.1109/TUTCAS.2001.946954
Filename
946954
Link To Document