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Value of Further Consideration. The value of further computation or thought is hard to determine. Neither humans nor computers can compute infinitely fast. Our rationality, or capability to determine actions with the highest utility, is therefore limited. The value of computation can be measured by comparing the expected utility of the decision that would be made now with the expected utility of a (it is hoped) better decision that might be made with further computation. The cost of computation can be measured by the time and resource cost or by opportunity cost.

Table learning methods do not generalize well. One approach that has received attention recently is to apply stronger learning algorithms to the task. A neural network (for example) will generalize from the data in the data-rich columns to supplement 271 the sparse information found in data-poor columns, often to great advantage. A starting point for work in this area is Musick (30). A third factor influencing accuracy stems from the fact that the majority of inference algorithms propagate and return point probabilities as answers.

A. Feigenbaum, The Handbook of Artificial Intelligence, volume 1, 2, and 3, Los Altos, CA: William Kaufmann, 1981. 3. T. M. Mitchell, Generalization as search, Artificial Intelligence, 18: 203–226, 1982. 4. D. A. Waterman, Generalization learning techniques for automating the learning of heuristics, Artificial Intelligence, 1 (1/2): 121–170, 1970. 5. N. J. Nilsson, The Mathematical Foundations of Learning Machines, Palo Alto, CA: Morgan Kaufman, 1990. 6. J. R. Koza, Genetic Programming, Cambridge, MA: The MIT Press, 1992.

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31.Knowledge and Data Engineering by John G. Webster (Editor)


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