TY  - GEN
T1  - Advances in large margin classifiers
T2  - Neural information processing series.
A2  - Smola, Alexander J.
LA  - English
PP  - Cambridge, Mass.
PB  - MIT Press
YR  - 2000
UL  - https://ebooks.jgu.edu.in/Record/ebsco_acadsubs_ocm62075949
AB  - The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
OP  - 412
CN  - Q325.5 .A34 2000eb
SN  - 9780262283977
SN  - 0262283972
SN  - 1423729544
SN  - 9781423729549
SN  - 0262292408
SN  - 9780262292405
SN  - 0262194481
SN  - 9780262194488
KW  - Machine learning.
KW  - Algorithms.
KW  - Kernel functions.
KW  - Computer algorithms.
KW  - Algorithms
KW  - Machine Learning
KW  - Apprentissage automatique.
KW  - Algorithmes.
KW  - Noyaux (Mathématiques)
KW  - algorithms.
KW  - COMPUTERS : Enterprise Applications : Business Intelligence Tools.
KW  - COMPUTERS : Intelligence (AI) & Semantics.
KW  - Computer algorithms
KW  - Kernel functions
KW  - Machine learning
KW  - COMPUTER SCIENCE/General
ER  -