loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: M. Pérez-Ortiz ; P. A. Gutiérrez and C. Hervás-Martínez

Affiliation: University of Córdoba, Spain

Keyword(s): Kernel Learning, Support Vector Machines, Ordinal Classification, Kernel-target Alignment.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Support Vector Machines and Applications ; Theory and Methods

Abstract: This paper deals with the idea of decomposing ordinal multiclass classification problems when working with kernel methods. The kernel parameters are optimised for each classification subtask in order to better adjust the kernel to the data. More flexible multi-scale Gaussian kernels are considered to increase the goodness of fit of the kernel matrices. Instead of learning independent models for all the subtasks, the optimum convex combination of the kernel matrices is then obtained, leading to a single model able to better discriminate the classes in the feature space. The results of the proposed algorithm shows promising potential for the acquisition of better suited kernels.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.225.149.158

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Pérez-Ortiz, M.; A. Gutiérrez, P. and Hervás-Martínez, C. (2014). Learning Kernel Label Decompositions for Ordinal Classification Problems. In Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA; ISBN 978-989-758-054-3, SciTePress, pages 218-225. DOI: 10.5220/0005079302180225

@conference{ncta14,
author={M. Pérez{-}Ortiz. and P. {A. Gutiérrez}. and C. Hervás{-}Martínez.},
title={Learning Kernel Label Decompositions for Ordinal Classification Problems},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA},
year={2014},
pages={218-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005079302180225},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2014) - NCTA
TI - Learning Kernel Label Decompositions for Ordinal Classification Problems
SN - 978-989-758-054-3
AU - Pérez-Ortiz, M.
AU - A. Gutiérrez, P.
AU - Hervás-Martínez, C.
PY - 2014
SP - 218
EP - 225
DO - 10.5220/0005079302180225
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic