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Maybe this is what you are looking for: Basically you would build and store a finite-state machine that resembles a trie with additional links between the various internal nodes using the given keywords. for the candidate document, go through it with the above finite-state machine. Then based on ...


You can increase no of hidden layers. Following is an example (But not very efficient)


You could say that the class of the data (e.g. spam vs not spam) is a hidden quality that can be inferred through the observable features (e.g. message subject contains "bitcoin"), $$P(C \; | \; F)$$ which says that the probability of the class $C$ is conditioned on the visibility of the feature $F$. Using Bayes' theorem we can write $$P(C \; | \; ...


The paper that appears to have introduced the term "softmax" is Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters (1989, NIPS) by John S. Bridle. As a side note, the softmax function (with base $b = e^{-\beta}$) $$\sigma (\mathbf {z} )_{i}={\frac {e^{-\beta z_{i}}}{\sum _{j=...

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