Pattern Recognition by Distributed Coding: Test and Analysis of the Power Space Similarity Method

Authors:
Takao Kobayashi;Masaki Nakagawa
Affiliations:
Tokyo University of Agriculture and Technology;Tokyo University of Agriculture and Technology
Venue:
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Year:
2004

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Cited 1

A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation

MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition

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Abstract

This paper considers pattern recognition methods using distributed coding. These methods permit rapid learning from a large number of training samples; their recognition speed is high regardless of the size of the learning samples. This paper presents both basic algorithm and extended algorithms. Experiments with a large database of off-line handwritten numeric patterns are then described using the power space similarity method, being a type of distributed coding. Finally the effectiveness of the technique is considered.