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Complexity Approximation Principle
 Computer Journal
, 1999
"... INTRODUCTION The subject of this note is another inductive principle, which can be regarded as a direct generalization of the minimum description length (MDL) and minimum message length (MML) principles. We will describe the work started at the Computer Learning Research Centre (Royal Holloway, Uni ..."
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Cited by 3 (2 self)
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INTRODUCTION The subject of this note is another inductive principle, which can be regarded as a direct generalization of the minimum description length (MDL) and minimum message length (MML) principles. We will describe the work started at the Computer Learning Research Centre (Royal Holloway, University of London) related to this new principle, which we call the complexity approximation principle (CAP). Both MDL and MML principles can be interpreted as Kolmogorov complexity approximation principles (as explained in Rissanen [1, 2] and Wallace and Freeman [3]; see also [4]). It is shown in [5] and [6] that it is possible to generalize Kolmogorov complexity to describe the optimal performance in different `games of prediction'. Using this general notion, called predictive complexity,itis straightforward to extend the MDL and MML principles to our more general CAP. In Section 2 we define predictive complexity, in Section 3 several examples are given and in Section 4
A Kolmogorov Complexitybased Genetic Programming Tool for String Compression
 in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2000), Darrell Whitley, David Goldberg, Erick CantuPaz, Lee Spector, Ian Parmee, and HansGeorg Beyer, Eds., Las Vegas
, 2000
"... By following the guidelines set in one of our previous papers, in this paper we face the problem of Kolmogorov complexity estimate for binary strings by making use of a Genetic Programming approach. This consists in evolving a population of Lisp programs looking for the "optimal" program that genera ..."
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By following the guidelines set in one of our previous papers, in this paper we face the problem of Kolmogorov complexity estimate for binary strings by making use of a Genetic Programming approach. This consists in evolving a population of Lisp programs looking for the "optimal" program that generates a given string. By taking into account several target binary strings belonging to different formal languages, we show the effectiveness of our approach in obtaining an approximation from the above of the Kolmogorov complexity function. Moreover, the adequate choice of "similar" target strings allows our system to show very interesting computational strategies. Experimental results indicate that our tool achieves promising compression rates for binary strings belonging to formal languages. Furthermore, even for more complicated strings our method can work, provided that some degree of loss is accepted. These results constitute a first step in using Kolmogorov complexit...
Trevors: Measuring the functional sequence complexity of proteins. Theor Biol Med Model
, 2007
"... This is an Open Access article distributed under the terms of the Creative Commons Attribution License ..."
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License
Quantitative Models from Qualitative Data: Case Studies in AgentBased Sociopolitical Modeling
"... Activity of the U.S. Government. This material is based upon work funded in whole or in part by the U.S. Government and any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Government. Quantit ..."
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Activity of the U.S. Government. This material is based upon work funded in whole or in part by the U.S. Government and any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Government. Quantitative Models from Qualitative Data: Case Studies in AgentBased Sociopolitical Modeling Many socioeconomic policy, planning and assessment questions arise because not enough is known about their subjects. While inaccessibility and lack of hard data are the very challenges that may make a computer model invaluable, they are also reasons why many modeling and simulation applications are never undertaken. The authors have found that qualitative agentbased models that are appropriately focused can prove surprisingly rich in quantitative data. Such models, accompanied by a thorough delineation of the applicable scope and context, have provided important insights into otherwise inscrutable systems. Building on early lessons learned in qualitative modeling (Dixon & Reynolds 2003), broader issues of qualitative modeling are explored. Case studies include negotiations, historical research, and leadership succession. 1
Thesis Proposal: Designing Distance Functions
, 2002
"... Distance function design is a fundamental problem underlying much of data mining. Numerous methods designed to solve a variety of problems ranging from clustering to nearestneighbor retrieval require some concept of distance with which to compare objects. Approaches can be classified as userdriven ..."
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Distance function design is a fundamental problem underlying much of data mining. Numerous methods designed to solve a variety of problems ranging from clustering to nearestneighbor retrieval require some concept of distance with which to compare objects. Approaches can be classified as userdriven methods, in which complex distance functions are induced from user input or other sources of labels and feedback
THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp. 000–000 RANDOM DEGREES OF UNBIASED BRANCHES
"... In our previous published research we discovered some very difficult to predict branches, called unbiased branches that have a “random ” dynamical behavior. We developed some improved state of the art branch predictors to predict successfully unbiased branches. Even these powerful predictors obtaine ..."
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In our previous published research we discovered some very difficult to predict branches, called unbiased branches that have a “random ” dynamical behavior. We developed some improved state of the art branch predictors to predict successfully unbiased branches. Even these powerful predictors obtained very modest average prediction accuracies on the unbiased branches while their global average prediction accuracies are high. These unbiased branches still restrict the ceiling of dynamic branch prediction and therefore accurately predicting unbiased branches remains an open problem. Starting from this technical challenge, we tried to understand in more depth what randomness is. Based on a hybrid mathematical and computer science approach we mainly developed some degrees of random associated to a branch in order to understand deeply what an unbiased branch is. These metrics are program’s Kolmogorov complexity, compression rate, discrete entropy and HMM prediction’s accuracy, that are useful for characterizing strings of symbols and particularly, our unbiased branches ’ behavior. All these random degree metrics could effectively help the computer architect to better understand branches ’ predictability, and also if the branch predictor should be improved related to the unbiased branches.
Automatic Semantic Network Construction for MultiLayer Annotation of Satellite Images
, 2008
"... A novel method is presented for annotating satellite images. The labels used for annotation are given by a user with a set of example images. A learning step is then applied to learn the model. The originality of the method is to formulate the problem of semantic annotation to a further extent than ..."
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A novel method is presented for annotating satellite images. The labels used for annotation are given by a user with a set of example images. A learning step is then applied to learn the model. The originality of the method is to formulate the problem of semantic annotation to a further extent than a mere probabilistic classification task. The method takes into account the semantical relationships between the concepts by considering a duality between the structure of the model and the structure of the set of labels. The semantical structure of the labels is represented by a semantic network containing three semantical relationships: synonymy, meronymy, and hyponymy. The semantic network is constrained in a hierarchy induced by the links of hyponymy and meronymy. By a procedure of MDL model selection, it is possible to find the optimal semantical structure of the set of labels. 1