In the theory of artificial neural networks winner-take-all networks are a case of competitive learning in recurrent neural networks. Output nodes in the network inhibit each other and activate themselves through reflexive connections. After some time, only one node in the output layer will be active.
In Stereo Matching algorithms, following the taxonomy proposed by Scharstein et al (IJCV 2002), winner-take-all is a local method for disparity computation. Adopting a winner-take-all strategy, the disparity associated with the minimum|maximum cost value is selected at each pixel.
It is axiomatic that in electronic commerce market, early dominant players such as AOL or Yahoo get most of the rewards. By 1998, one study found the top 5% of all web sites garnered more than 74% of all traffic.
Winner take all hypothesis suggests that once a technology or a firm gets ahead, it will do better and better over time, whereas, lagging technology and firms will get behind further.