scholarly journals Optimum decision rules in pattern recognition

1998 ◽  
pp. 726-735 ◽  
Author(s):  
Thien M. Ha
Keyword(s):  
Pattern Recognition ◽  
Decision Rules ◽  
Optimum Decision

scholarly journals The optimum decision rules in thesame-different paradigm

1996 ◽  
Vol 58 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Huanping Dai ◽  
Niek J. Versfeld ◽  
David M. Green
Keyword(s):  
Decision Rules ◽  
Optimum Decision

Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

2014 ◽  
Vol 886 ◽  
pp. 519-523 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Pattern Recognition Method ◽  
Two Dimensional ◽  
Recognition Method ◽  
Two Dimensional Image

Character Pattern recognition is widely used in the information technology field. This paper proposes a method of character pattern recognition based on rough set theory. By giving the characters two dimensional image, defining the location of the characteristic and abstracting the characteristic value, the knowledge table and table reduction can be ascertained. Then the decision rules can be deduced. Through the simulation of 26 English alphabets, the results illustrate this methods validity and correctness.

Optimum probabilistic processing in colour perception. I. Colour discrimination

1979 ◽  
Vol 205 (1159) ◽  
pp. 229-247 ◽  
Keyword(s):  
Statistical Estimation ◽  
Decision Rules ◽  
Colour Space ◽  
Spectral Parameters ◽  
Colour Perception ◽  
Optimum Decision ◽  
Statistical Estimation Theory ◽  
Wavelength Discrimination ◽  
Geometrical Concepts ◽  
Element Theory

A comprehensive account of wavelength discrimination and colour satu­ration discrimination is given in terms of optimum probabilistic signal detection. The theory is a logical deduction from statistical estimation theory of the visual estimate of the spectral parameters of the stimulus. In place of geometrical concepts associated with colour-space geometry, stimulus discriminability is determined by optimum decision rules given by likelihood ratio tests on statistics that are postulated for the trichro­matic responses. The classical line element theory and its formulations are deduced to be discriminability measures between signals. The different mathematical forms of classical theory are shown to correspond to differ­ent statistical constraints.

scholarly journals The optimum decision rules for the oddity task

1996 ◽  
Vol 58 (1) ◽  
pp. 10-21 ◽  
Author(s):  
Niek J. Versfeld ◽  
Huanping Dai ◽  
David M. Green
Keyword(s):  
Decision Rules ◽  
Oddity Task ◽  
Optimum Decision

Decision Making in a Case of Multiple Sclerosis — Preliminary Note

1981 ◽  
Vol 20 (01) ◽  
pp. 16-18 ◽  
Author(s):  
I. John
Keyword(s):  
Multiple Sclerosis ◽  
Decision Making ◽  
Pattern Recognition ◽  
Medical Education ◽  
Medical Students ◽  
Clinical Features ◽  
Decision Rules ◽  
Senior Resident ◽  
Simulated Patient ◽  
Preliminary Note

Decision making was studied in a case of multiple sclerosis with the aim of understanding what sort of decision rules may be used to accept the final hypothesis in coming to a diagnosis. 17 persons, including 4 expert neurologists, 1 senior resident and 12 medical students interviewed a simulated patient.It was found that the final hypothesis depended on the examination of a relatively small number of aspects (clinical features). Little search was made for aspects of choice alternatives other than the favoured one, namely multiple sclerosis. The results thus did not support pattern recognition as being important in decision making, and one may speculate that elimination by aspect or simple dominance rules may have been used in this case. The findings emphasise the apparent need to look for important distinguishing criteria in coming to a diagnosis.This may have significant implications for medical education in that students may have to concentrate on learning heavily weighted distinguishing cues rather than long lists of individual clinical features of diseases.

An Application of Binary Decision Trees to Pattern Recognition

2006 ◽  
Vol 10 (5) ◽  
pp. 682-687 ◽  
Author(s):  
Noboru Takagi ◽  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
Knowledge Discovery ◽  
Character Recognition ◽  
Decision Rules ◽  
Feature Space ◽  
Knowledge Discovery In Databases ◽  
Binary Decision ◽  
Japanese Character

Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.

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