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» Graph-based induction and its applications
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76
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AMC
2008
78views more  AMC 2008»
14 years 11 months ago
Information processing in complex networks: Graph entropy and information functionals
This paper introduces a general framework for defining the entropy of a graph. Our definition is based on a local information graph and on information functionals derived from the...
Matthias Dehmer
106
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KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 2 days ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
ECML
1998
Springer
15 years 3 months ago
Predicate Invention and Learning from Positive Examples Only
Previous bias shift approaches to predicate invention are not applicable to learning from positive examples only, if a complete hypothesis can be found in the given language, as ne...
Henrik Boström
104
Voted
AI
2011
Springer
14 years 6 months ago
Learning qualitative models from numerical data
Qualitative models are often a useful abstraction of the physical world. Learning qualitative models from numerical data sible way to obtain such an abstraction. We present a new ...
Jure Zabkar, Martin Mozina, Ivan Bratko, Janez Dem...
FOIS
2008
15 years 1 months ago
SKIing with DOLCE: toward an e-Science Knowledge Infrastructure
: An ontology of general science knowledge (SKIo) is developed to enhance machine representation and use of scientific theories in emerging e-Science Knowledge Infrastructures. SKI...
Boyan Brodaric, Femke Reitsma, Yi Qiang