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I know that logistic regression is for binary classification and softmax regression for multi-class problem. Would it be any differences if I train several logistic regression models with the same data and normalize their results to get a multi-class classifier instead of using one softmax model. I assume the result is of the same. Can I say : "all the multi-class classifier is the cascading result of binary classifiers". (except neuron network)
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Softmax regression (or multinomial logistic regression) is a generalization of logistic regression to the case where we want to handle multiple classes. In logistic regression we assumed that the labels were binary: y(i)∈{0,1} . We used such a classifier to distinguish between two kinds of hand-written digits.
The softmax function is used in multiclass classification methods such as neural networks, multinomial logistic regression, multiclass LDA, and Naive Bayes classifiers. The softmax function is used to output action probabilities in case of reinforcement learning.
The above Udacity lecture slide shows that Softmax function turns logits [2.0, 1.0, 0.1] into probabilities [0.7, 0.2, 0.1], and the probabilities sum to 1. Logits are the raw scores output by the last layer of a neural network.
Sigmoid is used for binary classification methods where we only have 2 classes, while SoftMax applies to multiclass problems. In fact, the SoftMax function is an extension of the Sigmoid function.
Echoing on what others have already conveyed.
classes.
Reference: http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/
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