6.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: Intro. to svm for Datamining
发信站: 南京大学小百合站 (Sun Jul 14 19:02:38 2002), 站内信件

An Introduction to Support Vector Machines for Data Mining

Robert Burbidge, Bernard Buxton
Computer Science Dept., UCL, Gower Street, WC1E 6BT, UK.

Abstract
With increasing amounts of data being generated by businesses and researchers
 there is a need for fast, accurate and robust algorithms for data analysis
.  Improvements in databases technology, computing performance and artificial
 intelligence have contributed to the development of intelligent data analysis
.  The primary aim of data mining is to discover patterns in the data that
 lead to better understanding of the data generating process and to useful
 predictions.  Examples of applications of data mining include detecting 
fraudulent credit card transactions, character recognition in automated zip
 code reading, and predicting compound activity in drug discovery.  Real-
world data sets are often characterized by having large numbers of examples
, e.g. billions of credit card transactions and potential ‘drug-like’ compo
unds
; being highly unbalanced, e.g. most transactions are not fraudulent, most
 compounds are not active against a given biological target; and, being corru
pted
 by noise.  The relationship between predictive variables, e.g. physical 
descriptors, and the target concept, e.g. compound activity, is often highly
 non-linear.  One recent technique that has been developed to address these
 issues is the support vector machine.  The support vector machine has been
 developed as robust tool for classification and regression in noisy, complex
 domains.  The two key features of support vector machines are generalization
 theory, which leads to a principled way to choose an hypothesis; and, kernel
 functions, which introduce non-linearity in the hypothesis space without
 explicitly requiring a non-linear algorithm.  In this tutorial I introduce
 support vector machines and highlight the advantages thereof over existing
 data analysis techniques, also are noted some important points for the data
 mining practitioner who wishes to use support vector machines.

全文见：
http://www.cs.ucl.ac.uk/staff/r.burbidge/pubs/yor12-svm-intro.html

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