Applications of generalized support vector machines to predictive modeling

Authors:
Nitin Agrarwal
Affiliations:
KXEN
Venue:
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2001

Citing 0
Cited 3

On fractal dimension in information systems. Toward exact sets in infinite information systems

Fundamenta Informaticae
On Asymptotic Properties of Rough-Set-Theoretic Approximations. Fractal Dimension, Exact Sets, and Rough Inclusion in Potentially Infinite Information Systems

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
On Fractal Dimension in Information Systems. Toward Exact Sets in Infinite Information Systems

Fundamenta Informaticae

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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.