Filtering noise in regression problems using a multiobjective leaning algorithm

D. A G Vieira; X. L. Travassos; Vasile Palade; R. R. Saldanha

doi:10.1109/ICTAI.2008.17

Filtering noise in regression problems using a multiobjective leaning algorithm

D. A G Vieira, X. L. Travassos, Vasile Palade, R. R. Saldanha

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Abstract

This paper applies a Neural Networks (NN) multiobjective learning algorithm called the Minimum Gradient Method (MGM) to filter noise in regression problems. This method is based on the concept that the learning is a bi-objective problem aiming at minimizing the empirical risk (training error) and the function complexity. The complexity is modeled as the norm of the network output gradient. After training, the NN behaves as an adaptive filter which minimizes the cross-validation error. The NN trained with this method can be used to pre-process the data and help reduce the signal-to-noise ratio (SNR). Some results are presented and they show the effectiveness of the proposed approach.

Original language	English
Title of host publication	Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Publisher	IEEE
Pages	452-456
Number of pages	5
Volume	2
ISBN (Print)	9780769534404
DOIs	https://doi.org/10.1109/ICTAI.2008.17
Publication status	Published - 2008
Externally published	Yes
Event	20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States Duration: 3 Nov 2008 → 5 Nov 2008

Conference

Conference	20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Country/Territory	United States
City	Dayton, OH
Period	3/11/08 → 5/11/08

Keywords

Inverse problems
Multiobjective training algorithms
Neural networks
Noise
Regression problems
Regularization methods

ASJC Scopus subject areas

Software
Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICTAI.2008.17

Cite this

Filtering noise in regression problems using a multiobjective leaning algorithm. / Vieira, D. A G; Travassos, X. L.; Palade, Vasile et al.
Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08. Vol. 2 IEEE, 2008. p. 452-456 4669808.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Vieira, DAG, Travassos, XL, Palade, V & Saldanha, RR 2008, Filtering noise in regression problems using a multiobjective leaning algorithm. in Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08. vol. 2, 4669808, IEEE, pp. 452-456, 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08, Dayton, OH, United States, 3/11/08. https://doi.org/10.1109/ICTAI.2008.17

@inproceedings{ac1ed96c3b0e4c549d0c9e221af52763,

title = "Filtering noise in regression problems using a multiobjective leaning algorithm",

abstract = "This paper applies a Neural Networks (NN) multiobjective learning algorithm called the Minimum Gradient Method (MGM) to filter noise in regression problems. This method is based on the concept that the learning is a bi-objective problem aiming at minimizing the empirical risk (training error) and the function complexity. The complexity is modeled as the norm of the network output gradient. After training, the NN behaves as an adaptive filter which minimizes the cross-validation error. The NN trained with this method can be used to pre-process the data and help reduce the signal-to-noise ratio (SNR). Some results are presented and they show the effectiveness of the proposed approach.",

keywords = "Inverse problems, Multiobjective training algorithms, Neural networks, Noise, Regression problems, Regularization methods",

author = "Vieira, {D. A G} and Travassos, {X. L.} and Vasile Palade and Saldanha, {R. R.}",

year = "2008",

doi = "10.1109/ICTAI.2008.17",

language = "English",

isbn = "9780769534404",

volume = "2",

pages = "452--456",

booktitle = "Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08",

publisher = "IEEE",

address = "United States",

note = "20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 ; Conference date: 03-11-2008 Through 05-11-2008",

}

TY - GEN

T1 - Filtering noise in regression problems using a multiobjective leaning algorithm

AU - Vieira, D. A G

AU - Travassos, X. L.

AU - Palade, Vasile

AU - Saldanha, R. R.

PY - 2008

Y1 - 2008

N2 - This paper applies a Neural Networks (NN) multiobjective learning algorithm called the Minimum Gradient Method (MGM) to filter noise in regression problems. This method is based on the concept that the learning is a bi-objective problem aiming at minimizing the empirical risk (training error) and the function complexity. The complexity is modeled as the norm of the network output gradient. After training, the NN behaves as an adaptive filter which minimizes the cross-validation error. The NN trained with this method can be used to pre-process the data and help reduce the signal-to-noise ratio (SNR). Some results are presented and they show the effectiveness of the proposed approach.

AB - This paper applies a Neural Networks (NN) multiobjective learning algorithm called the Minimum Gradient Method (MGM) to filter noise in regression problems. This method is based on the concept that the learning is a bi-objective problem aiming at minimizing the empirical risk (training error) and the function complexity. The complexity is modeled as the norm of the network output gradient. After training, the NN behaves as an adaptive filter which minimizes the cross-validation error. The NN trained with this method can be used to pre-process the data and help reduce the signal-to-noise ratio (SNR). Some results are presented and they show the effectiveness of the proposed approach.

KW - Inverse problems

KW - Multiobjective training algorithms

KW - Neural networks

KW - Noise

KW - Regression problems

KW - Regularization methods

UR - http://www.scopus.com/inward/record.url?scp=57649221170&partnerID=8YFLogxK

U2 - 10.1109/ICTAI.2008.17

DO - 10.1109/ICTAI.2008.17

M3 - Conference proceeding

AN - SCOPUS:57649221170

SN - 9780769534404

VL - 2

SP - 452

EP - 456

BT - Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08

PB - IEEE

T2 - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08

Y2 - 3 November 2008 through 5 November 2008

ER -

Filtering noise in regression problems using a multiobjective leaning algorithm

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this