Title :
Statistical Learning Theory and Support Vector Machines
Author :
Nasien, Dewi ; Yuhaniz, Siti S. ; Haron, Habibollah
Author_Institution :
Soft Comput. Res. Group, Univ. Teknol. Malaysia, Johor Bahru, Malaysia
Abstract :
It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted method for solving real-world problems. This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.
Keywords :
inference mechanisms; learning (artificial intelligence); pattern recognition; statistical analysis; support vector machines; SVM; decision making; inference problem; machine learning; pattern recognition; statistical learning theory; support vector machine; Artificial neural networks; Biological neural networks; Machine learning; Neurons; Pattern matching; Pattern recognition; Signal processing; Statistical learning; Support vector machine classification; Support vector machines; Pattern Recognition; Statistical Learning; Support Vector Machine;
Conference_Titel :
Computer Research and Development, 2010 Second International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-0-7695-4043-6
DOI :
10.1109/ICCRD.2010.183
