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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

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A dynamic programming approach to sequential pattern recognition.

K S Fu¹, Y T Chien, G P Cardillo

¹School of Elec. Engrg., Purdue University, Lafayette, Ind.; Dept. of Elec. Engrg. and Computer Sciences, University of California, Berkeley, Calif.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces dynamic programming for optimal sequential pattern recognition systems. This approach significantly reduces feature measurements needed for accurate classification, improving efficiency.

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Area of Science:

Computer Science
Artificial Intelligence
Pattern Recognition

Background:

Traditional pattern recognition systems often require extensive feature measurements.
The cost and efficiency of feature measurement are critical in practical applications.

Purpose of the Study:

To develop an optimal sequential pattern recognition system using dynamic programming.
To reduce the number of feature measurements required for classification.

Main Methods:

Dynamic programming and recursive optimization for designing multistage classifiers.
Dimensionality reduction techniques for statistically independent and Markov-dependent features.
Generalization for selecting optimal feature measurement sequences.

Main Results:

A sequential pattern classifier requiring fewer feature measurements than nonsequential methods.
Effective solutions for optimal sequential classification with independent and dependent features.
Demonstrated feasibility through computer simulations in character recognition.

Conclusions:

Dynamic programming offers an efficient approach to designing optimal sequential pattern recognition systems.
The proposed methods reduce computational dimensionality and feature measurement costs.
This framework is applicable to various pattern recognition tasks, including character recognition.