Pattern classification adopting multivariate polynomials

Kar Ann Toh

doi:10.1109/ISSNIP.2014.6827591

Pattern classification adopting multivariate polynomials

Kar Ann Toh

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
Publisher	IEEE Computer Society
ISBN (Print)	9781479928439
DOIs	https://doi.org/10.1109/ISSNIP.2014.6827591
Publication status	Published - 2014
Event	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore Duration: 2014 Apr 21 → 2014 Apr 24

Publication series

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

Other

Other	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
Country/Territory	Singapore
City	Singapore
Period	14/4/21 → 14/4/24

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Information Systems

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10.1109/ISSNIP.2014.6827591

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).

@inproceedings{301154312a3c49e494ad6544a9a575da,

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.",

author = "Toh, {Kar Ann}",

year = "2014",

doi = "10.1109/ISSNIP.2014.6827591",

language = "English",

isbn = "9781479928439",

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

publisher = "IEEE Computer Society",

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

address = "United States",

note = "9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 ; Conference date: 21-04-2014 Through 24-04-2014",

}

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 proceeding › Conference contribution

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T1 - Pattern classification adopting multivariate polynomials

AU - Toh, Kar Ann

PY - 2014

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N2 - 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.

AB - 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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M3 - Conference contribution

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SN - 9781479928439

T3 - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

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

PB - IEEE Computer Society

T2 - 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014

Y2 - 21 April 2014 through 24 April 2014

ER -

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). doi: 10.1109/ISSNIP.2014.6827591

Pattern classification adopting multivariate polynomials

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