Modeling object and action in an image

Given an object recognition system, I obtain the confident values of whether or not an object appears in an image.

I want to find out whether or not an action is likely to happen given such objects' appearance probability. I can model such a system using conditional probability, e.g., the probability of action A given the appearance of object O1, and without the appearance of object O2, etc.

Could Topic models or any LDA-style model help in this case?

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Skip-gram model and negative sampling

In the previous post , we have seen the 3 word2vec models: skip-gram, CBOW and GloVe. Now let's have a look at negative sampling and what it is used to make training skip-gram faster. The idea is originated from this paper: " Distributed Representations of Words and Phrases and their Compositionality ” (Mikolov et al. 2013) In the previous example , we have seen that if we have a vocabulary of size 10K, and we want to train word vectors of size 300. Then the number of parameters we have to estimate in each layer is 10Kx300. This number is big and makes training prone to over-fitting and gives too much focus on words that appear often, and less focus on rare words. Subsampling of frequent words So the idea of subsampling is that: we try to maximize the probability that "real outside word" appears, and minimize the probability that "random words" appear around center word. Real outside words are words that characterize the meaning of the center word, wh

Spam and Bayes' theorem

I divide my email into three categories: A1 = spam. A2 = low priority, A3 = high priority. I find that: P(A1) = .7 P(A2) = .2 P(A3) = .1 Let B be the event that an email contains the word "free". P(B|A1) = .9 P(B|A2) = .01 P(B|A3) = .01 I receive an email with the word "free". What is the probability that it is spam?

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