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== ''' Course Note for Sept.30th''' ('''Classfication_by Liang Jiaxi''') == | == ''' Course Note for Sept.30th''' ('''Classfication_by Liang Jiaxi''') == | ||
'''1. '''<br /> | |||
'''2. Classification'''<br /> | |||
Classification is a function between two random varialbe | |||
'''3. Error data'''<br /> | |||
Definition: | |||
''True error rate'' of a classifier(h) is defined as the probability that the prediction of Y from X do not exactly equal to Y, namely, <math>\, L(h)=P(h(X)\neqY)</math>. | |||
''Empirical error rate(training error rate)'' of a classifier(h) is | |||
'''4. Bayes Classifier'''<br /> | |||
Revision as of 15:35, 30 September 2009
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Course Note for Sept.30th (Classfication_by Liang Jiaxi)
1.
2. Classification
Classification is a function between two random varialbe
3. Error data
Definition:
True error rate of a classifier(h) is defined as the probability that the prediction of Y from X do not exactly equal to Y, namely, [math]\displaystyle{ \, L(h)=P(h(X)\neqY) }[/math].
Empirical error rate(training error rate) of a classifier(h) is
4. Bayes Classifier