Information analysis
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OpenCV OpenCV is an open-source library for computer vision. It has hooks in C, C++ and Python, numerous artificial vision routines and is portable across systems. If you want to develop an open-source computer-vision program, OpenCV is a library of choice.
Simple face recognition using OpenCV « The Pebibyte
The PMI of a pair of outcomes x and y belonging to discrete random variables X and Y quantifies the discrepancy between the probability of their coincidence given their joint distribution and the probability of their coincidence given only their individual distributions, assuming independence. Mathematically: The mutual information (MI) of the random variables X and Y is the expected value of the PMI over all possible outcomes.
Pointwise mutual information - Wikipedia, the free encyclopedia
Pointwise mutual information - MaltCourses
This quantity is zero if x and y are independent, positive if they are positively correlated, and negative if they are negatively correlated. In Turney, ACL 2002 this was used as a way of assessing the semantic orientation of words or phrases. Specifically the semantic orientation of x was defined as S O ( x ) = P M I ( x ,' e x c e l l e n t ') − P M I ( x ,' p o o r ')
Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 417–424.
Turney, ACL 2002 - MaltCourses
Individual (H(X),H(Y)), joint (H(X,Y)), and conditional entropies for a pair of correlated subsystems X,Y with mutual information I(X; Y). In probability theory and information theory , the mutual information (sometimes known by the archaic term transinformation ) of two random variables is a quantity that measures the mutual dependence of the two random variables. The most common unit of measurement of mutual information is the bit , when logarithms to the base 2 are used. where p ( x , y ) is the joint probability distribution function of X and Y , and and are the marginal probability distribution functions of X and Y respectively.
Mutual information - Wikipedia, the free encyclopedia