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JACIII Vol.14 No.4 pp. 325-343
doi: 10.20965/jaciii.2010.p0325
(2010)

Paper:

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An Extension Approach for Neural Networks by Introducing a Nearest Neighbor Algorithm in Relative Coordinates

Hirofumi Suzaki* and Satoru Kuhara**

*Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan

**Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan

Received:
July 30, 2009
Accepted:
December 25, 2009
Published:
May 20, 2010
Creative Commons License
Keywords:
neural network, nearest neighbor algorithm, network inference, curse of dimensionality, generalized mean
Abstract
Computational models known as neural networks discriminate among different types of nonlinear data, enabling the design of flexible calculation through machine-learning algorithms. Thanks to the simplicity of calculation, the nearest neighbor algorithm is a well-studied classification method. If the nearest neighbor algorithm inference is shown by a network model consisting of a neuron model representing data, it may become deterministic with adjustable parameters. We propose a new neuron model using the generalized mean and have designed a practical neural network framework based on the nearest neighbor algorithm. Because our proposed parallel distributed processing model is not simply a distance comparison between two points, it uses information from a whole body of data. This makes our classification superior to the nearest neighbor algorithm for algorithmic principles.
Cite this article as:
H. Suzaki and S. Kuhara, “An Extension Approach for Neural Networks by Introducing a Nearest Neighbor Algorithm in Relative Coordinates,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.4, pp. 325-343, 2010.
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