Boulton, D. M. and Wallace, C. S. (1975) An information measure for single-link classification. Comput. J., 18, 236-- 238.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....implementing them listed below. 3.1 Decision Trees Decision trees are perhaps the most widely studied inductive learning models in the machine learning community. The literature abounds with papers proposing new models or variations of existing models and case studies using decision trees ([14, 21, 22, 25, 30, 34, 22 40, 43, 49, 50, 51, 53, 89, 93, 98, 99, 100, 101, 102, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 118, 120, 123, 126, 129, 130, 131, 133, 134, 136]) For this case study, we use decision tree software from Quinlan and Buntine. Quinlan introduces decision trees and illustrates the use of his C4.5 software for decision trees (c4.5tree) and production rules derived therefrom (c4.5rule) in [105] Several decision tree algorithms (cart, id3, c4, ....

D.M. Boulton and C.S. Wallace (1973). An information measure for single link classification. The Computer Journal 18(3). 236-238.

....complexity, Solomonoff probabilities are defined with respect to some particular UTM, but universality guarantees that different choices of UTM will affect the odds between future events only by a factor with bounds independent of S, E1andE2. The third stream was introduced by Wallace and Boulton [7, 8, 9, 10, 11, 12], with a similar but independent development by Rissanen [3] Unlike the other streams, its basis is Shannon s theory of information rather than the theory of Turing machines. Like the second stream, it regards the given string S as being a representation, in some code, of data about the real ....

Boulton, D. M. and Wallace, C. S. (1975) An information measure for single-link classification. Comput. J., 18, 236-- 238.

....Solomonoff probabilities are defined with respect to some particular UTM, but universality guarantees that different choices of UTM will affect the odds between future events only by a factor with bounds independent of S, E1 and E2. The third stream was introduced by Wallace and Boulton [7, 8, 9, 10, 11, 12], with a similar but independent development by Rissanen [3] Unlike the other streams, its basis is Shannon s theory of information rather than the theory of Turing machines. Like the second stream, it regards the given string S as being a representation, in some code, of data about the real ....

D. M. Boulton and C. S. Wallace, An information measure for single-link classification, Computer J. 18 (3) (August 1975) 236--238.

....H. Hence the name minimummessage length (principle) for thus choosing a theory, H, to fit observed data, D. The principle seems to have first been stated by Solomonoff[31, p20] and was re stated and apparently first applied in a series of papers by Wallace and Boulton[37, p185] 4] 5, pp63 64][6, 8, 7, 38] dealing with model selection and parameter estimation (for Normal and multi state variables) for problems of mixture modelling (also known as clustering, numerical taxonomy or, e.g. 3] intrinsic classification ) An important special case of the Minimum Message Length principle is an ....

D.M. Boulton and C.S. Wallace. An information measure for single-link classification. The Computer Journal, 18(3):236--238, August 1975.

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Boulton, D., & Wallace, C. (1973b). An information measure for single-link classification. Computer Journal, 18, 236--238.

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