Softmax Regression
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<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 | |
| + | <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 | ||
were <math>y^{(i)} \in \{0,1\}</math>. Our hypothesis took the form: | were <math>y^{(i)} \in \{0,1\}</math>. Our hypothesis took the form: | ||
