Authors:
Wenjun Xia
and
Tadashi Shibata
Affiliation:
The University of Tokyo, Japan
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.
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