Code covered by the BSD License
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[Classification_accuracy,p1,m...
Inputs:
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[Mean_accuracy, Variance,p1,m...
Inputs:
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calcfit(data, ideals, y)
Calculates the classification accuracy for the given data and class
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classifier(data, ideals, y)
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h=owa(A,w)
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idealvectors(data, y)
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init_data(data,v,c)
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pmean(x,p)
Calculates generalized mean
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re=weight1(n,m)
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w=weight2(n,m)
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w=weight3(n,m)
exponential linguistic quantifier: Q(r)=exp(-alpha*r)
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w=weight4(n,m)
trigonometric linguistic quantifier: Q(r)=asin(r*alpha)
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weights=Ohaganw(n,alpha)
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example1.m
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example2.m
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mainfile1.m
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View all files
Similarity classifier with OWA operators
by Pasi Luukka
01 Nov 2012
(Updated 10 Dec 2012)
Toolbox presents vector based classification method which uses similarity measures and OWA operators
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| File Information |
| Description |
Similarity classifier with OWA operators toolbox presents vector based classification method which uses similarity measures and OWA operators to make a distiction to which class samples belong. It creates ideal vectors for each class and then uses similarity to measure how similar the samples are compared to each ideal vector. These similarity vectors are then aggregated by OWA operators. For more information about the method see the original publication:
P. Luukka, O. Kurama, Similarity classifier with ordered weighted averaging operators,
Expert Systems With Applications, 40, (2013), pp. 995-1002 |
| MATLAB release |
MATLAB 7.12 (R2011a)
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| Updates |
| 10 Dec 2012 |
Read me file is updated. |
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