Support vector machines processing system

Details Support vector machines processing system Support vector machines processing system

2004-08-27

2009-02-10

Hirl, Joseph P (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

C706S046000, C706S012000

Reexamination Certificate

active

07490071

ABSTRACT:
An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, based on a plurality of model-building parameters, a parameter estimation unit operable to estimate values for at least some of the model-building parameters, and an apply unit operable to apply the support vector machine model using the data stored in the system.

REFERENCES:
Kristin P. Bennett et al., Support Vector Machines: Hype or Hallelujah?, 2000, ACM SIGKDD, 1-13.
Nello Cristianini, An Introduction to Support vector Machines and other kernel-based learning methods, 2000, Cambridge, Cover and title page.
Tom Howley et al., The Genetic evolution of Kernels for Support vector Machine Classifiers, 2004, National University of Ireland @ Galway.
Yiming Yang, A Scalability Analysis of Classifiers in Text Categorization, ACM SIG Alternate.cls, 2003, 96-103.
Andrea Boni et al., Digital Architectures for Adaptive Processing of Measurement Data, 2004, University of Trento.
S. Sathiya et al., Building Support Vector Machines with Reduced Classifier Complexity, 2006, Journal of Machine Learning Research, 1493-1515.
Nello Cristianini et al., An Introduction to Support Vector Machines, Mar. 2000, University of Cambridge, 103-122.

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