Softmax Regression

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(Introduction)
(Introduction)
Line 10: Line 10:
<math>\{ (x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)}) \}</math>
<math>\{ (x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)}) \}</math>
of <math>m</math> labeled examples, where the input features are <math>x^{(i)} \in \Re^{n+1}</math>.   
of <math>m</math> labeled examples, where the input features are <math>x^{(i)} \in \Re^{n+1}</math>.   
-
(In these set of notes, we will use the notational convention of letting <math>x^{(i)}</math> be
+
(In this set of notes, we will use the notational convention of letting <math>x^{(i)}</math> be
<math>n+1</math> dimensional, with <math>x_0 = 1</math> corresponding to the intercept term.)  
<math>n+1</math> dimensional, with <math>x_0 = 1</math> corresponding to the intercept term.)  
With logistic regression, we were in the binary classification setting, so the labels  
With logistic regression, we were in the binary classification setting, so the labels  

Revision as of 18:28, 10 May 2011

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