Title :
Kernel machine learning: a systems perspective
Author :
Cauwenberghs, Gert
Author_Institution :
Johns Hopkins Univ., MD, USA
Abstract :
The article presents a systems perspective on kernel machine learning, including a discussion of margin and generalization, support vector machines and kernels. Cost functions and dual formulation are covered including classification, regression and probability estimation. The article concludes by analysing sparsity, incremental learning and learning machines
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); learning automata; classification; cost function; dual formulation; generalization; incremental learning; kernel machine learning; learning machine; margin; probability estimation; regression; sparsity; support vector machine; Kernel; Machine learning; Statistical learning; Support vector machines;
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.2001.946953
