Abstract #302391

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JSM 2003 Abstract #302391
Activity Number: 65
Type: Other
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: ASA
Abstract - #302391
Title: Kernel Methods for Pattern Analysis
Author(s): Nello Cristianini*+
Companies: University of California, Davis
Address: Department of Statistics, Davis, CA, 95616,
Keywords:
Abstract:

Pattern analysis deals with the statistical and computational problem of detecting and exploiting general types of relations (patterns) in datasets. Data are intended in a very general sense, and may include data structures as diverse as strings, graphs, trees, free text, among others. Kernel methods are a very efficient new class of pattern analysis algorithms based on the idea of 'implicitly embedding' data into high-dimensional spaces and using linear methods to detect relations in such spaces.Their statistical and computational behavior are currently the object of intense research within the pattern recognition, machine learning, and statistics communities. The computational side involves tools from optimization, while the statistical side is mostly concerned with tools from the theory of empirical processes, such as concentration inequalities and uniform convergence. Among the main appealing features of the class of algorithms is the possibility to perform operations such as principal components or linear discriminant analysis on data such as strings of different length, or images. The best known element of this class if the support vector machine algorithm.

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