Title :
Statistical learning and VC theory
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;
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
DOI :
10.1109/TUTCAS.2001.946954
