Results 1 
6 of
6
Minimum Message Length and Kolmogorov Complexity
 Computer Journal
, 1999
"... this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465] ..."
Abstract

Cited by 104 (25 self)
 Add to MetaCart
this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465]
Constructing XofN Attributes for Decision Tree Learning
 Machine Learning
, 1998
"... . While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, XofN. An XofN representation is a set containing one or more attributevalue pairs. ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, XofN. An XofN representation is a set containing one or more attributevalue pairs. For a given instance, the value of an XofN representation corresponds to the number of its attributevalue pairs that are true of the instance. A single XofN representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single MofN representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one XofN representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of XofN representations is carrie...
MML mixture modelling of multistate, Poisson, von Mises circular and Gaussian distributions
 In Proc. 6th Int. Workshop on Artif. Intelligence and Statistics
, 1997
"... Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an informationtheoretic and a Bayesian interp ..."
Abstract

Cited by 8 (5 self)
 Add to MetaCart
Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an informationtheoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob (Wallace and Boulton (1968), Wallace (1986), Wallace and Dowe(1994)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components. The message length is (to within a constant) the logarithm of the posterior probability of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated, and permits multivariate data from Gaussian, discrete multistate, Poisson and von Mises circular dist...
Intrinsic Classification by MML—the Snob Program
 Proc. Seventh Australian Joint Conf. Artificial Intelligence
, 1994
"... Abstract: We provide a brief overview ofMinimum Message Length (MML) inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)). We then outline how MML is used for statistical parameter estimation, and how the MML intrinsic classification program, Snob (Wallace and Boulton (1968), ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
Abstract: We provide a brief overview ofMinimum Message Length (MML) inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)). We then outline how MML is used for statistical parameter estimation, and how the MML intrinsic classification program, Snob (Wallace and Boulton (1968), Wallace (1986), Wallace (1990)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with model selection in intrinsic classification. We mention here the most recent extensions to Snob, permitting Poisson and von Mises circular distributions. We also survey some applications of Snob (albeit briefly), and further provide some documentation on how the user can guide Snob’s search through various models of the given data to try to obtain that model whose message length is a minimum.
Hierarchical clusters of vegetation types
"... Abstract: In this paper, we examine possible sources of hierarchical (nested) structure in vegetation data. We then use the Minimum Message length principle to provide a rational means of comparing hierarchical and nonhierarchical clustering. The results indicate that, with the data used, a hierarc ..."
Abstract
 Add to MetaCart
Abstract: In this paper, we examine possible sources of hierarchical (nested) structure in vegetation data. We then use the Minimum Message length principle to provide a rational means of comparing hierarchical and nonhierarchical clustering. The results indicate that, with the data used, a hierarchical solution was not as efficient as a nonhierarchical one. However, the hierarchical solution seems to provide a more comprehensible solution, separating first isolated types, probably caused from unusual contingent events, then subdividing the more diverse areas before finally subdividing the less diverse. By presenting this in 3 stages, the complexity of the nonhierarchical result is avoided. The result also suggests that a hierarchical analysis may be useful in determining ‘homogeneous ’ areas. Abbreviatons: MML Minimum Message Length; MUAP Modifiable unit area problem.
AND
"... We explore the use of Rissanen’s minimum description length principle for the construction of decision trees. Empirical results comparing this approach to other methods are given. 0 1989 Academic Press, Inc. 1. ..."
Abstract
 Add to MetaCart
We explore the use of Rissanen’s minimum description length principle for the construction of decision trees. Empirical results comparing this approach to other methods are given. 0 1989 Academic Press, Inc. 1.