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Minimum description length

Known as: MDL, Minimum Description Length Principle 
The minimum description length (MDL) principle is a formalization of Occam's razor in which the best hypothesis for a given set of data is the one… Expand
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Papers overview

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Highly Cited
2014
Highly Cited
2014
Matrix factorizations—where a given data matrix is approximated by a product of two or more factor matrices—are powerful data… Expand
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Highly Cited
2010
Highly Cited
2010
  • J. Rissanen
  • Encyclopedia of Machine Learning
  • 2010
  • Corpus ID: 26228557
 
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Highly Cited
2000
Highly Cited
2000
The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify… Expand
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Highly Cited
1998
Highly Cited
1998
The two dominant schemes for rule-learning, C4.5 and RIPPER, both operate in two stages. First they induce an initial rule set… Expand
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Highly Cited
1994
Highly Cited
1994
The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural… Expand
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Highly Cited
1994
Highly Cited
1994
A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's minimal… Expand
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Highly Cited
1993
Highly Cited
1993
An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of… Expand
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Highly Cited
1993
Highly Cited
1993
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the… Expand
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Highly Cited
1991
Highly Cited
1991
The authors introduce an index of resolvability that is proved to bound the rate of convergence of minimum complexity density… Expand
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Highly Cited
1989
Highly Cited
1989
Abstract We explore the use of Rissanen's minimum description length principle for the construction of decision trees. Empirical… Expand
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