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Applications of generalized support vector machines to predictive modeling

Published:26 August 2001Publication History

ABSTRACT

The work of the Russian mathematician Vladimir Vapnik (AT&T Labs) enables us to go back to the roots of theoretical statistics, leaving behind Fisher's parameters in favor of the general approaches started in the 1930s by Glivenko-Cantelli-Kolmogorov. Nowadays, it has become possible to model millions of events described by thousands of variables, within a reasonable time for a specific application. The SRM approach works with a family of models and calibrates the family of models to a point which is the best compromise between accuracy and robustness. It also measures the complexity of the model using VC dimension which is not plagued by number of parameters. Hence models for large events described by several parameters can be generalized. This opens up great prospects in numerous fields like Customer Relationship Management, Network Optimization, Risk Management, Manufacturing Yield Management, and a number of other data-rich problems.

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  • Published in

    cover image ACM Conferences
    KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2001
    493 pages
    ISBN:158113391X
    DOI:10.1145/502512

    Copyright © 2001 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 26 August 2001

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