Quote:
Originally Posted by Dave Schwartz
With a NN, there is a threshold score (between 0.00 and 1.00) for an event happening. Take a basketball game. This is simplistic, but the way it typically works is that if the score is above 0.90 you bet the home team and below 0.10 you bet the visitor (or favorite/dog, whatever).
In other words, there is a 10% "margin for error." Now recall that the system is trying to get every "fact" right. Essentially, its goal is to get 100% of the games/races 90% right.
What we need is a system that tries to get 90% of the races 100% right. In other words, it needs to be able to say, "Hey, there are just some races that I can't get.
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Excellent insight as usual, your posts are much appreciated. In my brief foray into AI I ended up trying to decipher the Dempster-Shafer Theory.
The probability contains two components represented by Belief and Plausibility. As per usual I got hopelessly lost in the Math.
http://en.wikipedia.org/wiki/Dempster-Shafer_theory
http://www.glennshafer.com/