Pattern classification adopting multivariate polynomials

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation formulation based on a ridge regression beyond the commonly known primal and dual forms. Extensive experiments are performed to observe the predictor learning properties on polynomial models beyond the frequently adopted second order.

Original languageEnglish
Title of host publicationIEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PublisherIEEE Computer Society
ISBN (Print)9781479928439
DOIs
Publication statusPublished - 2014 Jan 1
Event9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore
Duration: 2014 Apr 212014 Apr 24

Publication series

NameIEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

Other

Other9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
CountrySingapore
CitySingapore
Period14/4/2114/4/24

Fingerprint

Pattern recognition
Polynomials
Experiments
Statistical Models

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Toh, K. A. (2014). Pattern classification adopting multivariate polynomials. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings [6827591] (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). IEEE Computer Society. https://doi.org/10.1109/ISSNIP.2014.6827591
Toh, Kar Ann. / Pattern classification adopting multivariate polynomials. IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).
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title = "Pattern classification adopting multivariate polynomials",
abstract = "The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation formulation based on a ridge regression beyond the commonly known primal and dual forms. Extensive experiments are performed to observe the predictor learning properties on polynomial models beyond the frequently adopted second order.",
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Toh, KA 2014, Pattern classification adopting multivariate polynomials. in IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings., 6827591, IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, IEEE Computer Society, 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014, Singapore, Singapore, 14/4/21. https://doi.org/10.1109/ISSNIP.2014.6827591

Pattern classification adopting multivariate polynomials. / Toh, Kar Ann.

IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. 6827591 (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Toh KA. Pattern classification adopting multivariate polynomials. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society. 2014. 6827591. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). https://doi.org/10.1109/ISSNIP.2014.6827591