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Support Vector Machines
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Linear Classifiers
y est denotes +1 denotes -1 How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore f x f ( x , w ,b ) = sign( w . x - b )
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Linear Classifiers f
x y est denotes +1 denotes -1 f ( x , w ,b ) = sign( w . x - b ) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore
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Linear Classifiers f
x y est denotes +1 denotes -1 f ( x , w ,b ) = sign( w . x - b ) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore
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Linear Classifiers f
x y est denotes +1 denotes -1 f ( x , w ,b ) = sign( w . x - b ) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore
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Linear Classifiers f
x y est denotes +1 denotes -1 f ( x , w ,b ) = sign( w . x - b ) How would you classify this data? Copyright © 2001, 2003, Andrew W. Moore
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Maximum Margin f
x y est denotes +1 denotes -1 f ( x , w ,b ) = sign( w . x - b ) The maximum margin linear classifier is the linear classifier with the maximum margin. This is the simplest kind of SVM (Called an LSVM) Linear SVM Copyright © 2001, 2003, Andrew W. Moore
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Kernel function
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Text Categorization Inductive
learning Inpute : Output : f(x) = confidence(class) In the case of text classification ,the attribute are words in the document ,and the classes are the categories.
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Text Categorization Results
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