Softmax Regression
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regression's parameters are "redundant." More formally, we say that our | regression's parameters are "redundant." More formally, we say that our | ||
softmax model is '''overparameterized,''' meaning that for any hypothesis we might | softmax model is '''overparameterized,''' meaning that for any hypothesis we might | ||
- | fit to the data, there | + | fit to the data, there are multiple parameter settings that give rise to exactly |
the same hypothesis function <math>h_\theta</math> mapping from inputs <math>x</math> | the same hypothesis function <math>h_\theta</math> mapping from inputs <math>x</math> | ||
to the predictions. | to the predictions. | ||
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classifier would be appropriate. In the second case, it would be more appropriate to build | classifier would be appropriate. In the second case, it would be more appropriate to build | ||
three separate logistic regression classifiers. | three separate logistic regression classifiers. | ||
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+ | {{Softmax}} | ||
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+ | {{Languages|Softmax回归|中文}} |