loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Wenjun Xia and Tadashi Shibata

Affiliation: The University of Tokyo, Japan

ISBN: 978-989-8425-84-3

Keyword(s): Nearest neighbor, Template reduction, k-Means clustering, Hardware implementation.

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 ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Dealing with large data sets, the computational cost and resource demands using the nearest neighbor (NN) classifier can be prohibitive. Aiming at efficient template condensation, this paper proposes a template re-duction algorithm for NN classifier by introducing the concept of critical boundary vectors in conjunction with K-means centers. Initially K-means centers are used as substitution for the entire template set. Then, in order to enhance the classification performance, critical boundary vectors are selected according to a newly proposed training algorithm which completes with only single iteration. COIL-20 and COIL-100 databases were utilized for evaluating the performance of image categorization in which the bio-inspired directional-edge-based image feature representation (Suzuki and Shibata. 2004) was employed. UCI iris and UCI Landsat databases were also utilized to evaluate the system for other classification tasks using numerical-valued vectors. Experimental results show that by using the reduced template sets, the proposed algorithm shows a superior performance to NN classifier using all samples, and comparable to Support Vector Machines using Gaussian kernel which are computationally more expensive. (More)

PDF ImageFull Text

Download
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 18.205.109.82

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:
Xia, W. and Shibata, T. (2011). CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 93-98. DOI: 10.5220/0003642600930098

@conference{ncta11,
author={Wenjun Xia. and Tadashi Shibata.},
title={CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={93-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003642600930098},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION
SN - 978-989-8425-84-3
AU - Xia, W.
AU - Shibata, T.
PY - 2011
SP - 93
EP - 98
DO - 10.5220/0003642600930098

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.