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- Softmax_function abstract "In mathematics, in particular probability theory and related fields, the softmax function is a generalization of the logistic function that maps a length-p vector of real values to a length-K vector of values, defined as:Since the vector sums to one and all its elements are strictly between zero and one, they represent a categorical probability distribution. For this reason, the softmax function is used in various probabilistic multiclass classification methods including multinomial logistic regression, multiclass linear discriminant analysis, naive Bayes classifiers and neural networks. Specifically, in multinomial logistic regression and linear discriminant analysis, the input to the function is the result of K distinct linear functions, and the predicted probability for the j'th class given a sample vector x is:".
- Softmax_function wikiPageID "6152185".
- Softmax_function wikiPageRevisionID "606771645".
- Softmax_function subject Category:Computational_neuroscience.
- Softmax_function subject Category:Log-linear_models.
- Softmax_function subject Category:Neural_networks.
- Softmax_function comment "In mathematics, in particular probability theory and related fields, the softmax function is a generalization of the logistic function that maps a length-p vector of real values to a length-K vector of values, defined as:Since the vector sums to one and all its elements are strictly between zero and one, they represent a categorical probability distribution.".
- Softmax_function label "Softmax function".
- Softmax_function sameAs m.0fswxg.
- Softmax_function sameAs Q7554146.
- Softmax_function sameAs Q7554146.
- Softmax_function wasDerivedFrom Softmax_function?oldid=606771645.
- Softmax_function isPrimaryTopicOf Softmax_function.