loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Xijiong Xie

Affiliation: Ningbo University, China

Keyword(s): Non-parallel Hyperplane Classifier, Least Squares Twin Support Vector Machines, Manifold-preserving Graph Reduction.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image Understanding ; Image-Based Modeling ; Kernel Methods ; Pattern Recognition ; Software Engineering ; Sparsity ; Theory and Methods

Abstract: Least squares twin support vector machines are a new non-parallel hyperplane classifier, in which the primal optimization problems of twin support vector machines are modified in least square sense and inequality constraints are replaced by equality constraints. In classification problems, enhancing the robustness of least squares twin support vector machines and reducing the time complexity of kernel function evaluation of a new example when inferring the label of a new example are very important. In this paper, we propose a new sparse least squares twin support vector machines based on manifold-preserving graph reduction which is an efficient graph reduction algorithm with manifold assumption. This method first selects informative examples for positive examples and negative examples, respectively and then applies them for classification. Experimental results confirm the feasibility and effectiveness of our proposed method.

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 52.14.126.74

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:
Xie, X. (2018). Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-276-9; ISSN 2184-4313, SciTePress, pages 563-567. DOI: 10.5220/0006690805630567

@conference{icpram18,
author={Xijiong Xie.},
title={Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2018},
pages={563-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006690805630567},
isbn={978-989-758-276-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction
SN - 978-989-758-276-9
IS - 2184-4313
AU - Xie, X.
PY - 2018
SP - 563
EP - 567
DO - 10.5220/0006690805630567
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