ISSN:
1572-8471
Keywords:
creativity
;
explanatory induction
;
informativeness
;
intensional complexity
;
machine learning
;
MDL principle
;
model evaluation
;
Occam's Razor
;
scientific and knowledge discovery
Source:
Springer Online Journal Archives 1860-2000
Topics:
Natural Sciences in General
Notes:
Abstract The Minimum Description Length (MDL) principle is the modernformalisation of Occam's razor. It has been extensively and successfullyused in machine learning (ML), especially for noisy and long sources ofdata. However, the MDL principle presents some paradoxes andinconveniences. After discussing all these, we address two of the mostrelevant: lack of explanation and lack of creativity. We present newalternatives to address these problems. The first one, intensionalcomplexity, avoids extensional parts in a description, so distributingcompression ratio in a more even way than the MDL principle. The secondone, information gain, forces that the hypothesis is informative (orcomputationally hard to discover) wrt. the evidence, so giving a formaldefinition of what is to discover.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1011350914776
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