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
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.
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