Thanks, I'll check out that text. Chris
On 1/8/07, belinda thom <[EMAIL PROTECTED]> wrote:
I'm doing something similar right now (although I'm not at present using conx). I used the algorithm Tom Mitchell suggests at the end of his 1st chapter in Machine Learning (a textbook). When you're assuming a linear activation function, and I don't believe this method is any different for non-linear cases. Updates are easily done for _each_ play in the game as follows: your current training estimate of the value of a state is compared to the value the function estimates for the "best" (in a minimax sense) next state (when the player will next play). Check out his text, its pretty clear. HTH, --b
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