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» A statistical approach to rule learning
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ICML
2000
IEEE
16 years 7 months ago
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
Paul Komarek, Andrew W. Moore
ICASSP
2011
IEEE
14 years 10 months ago
Sparse coding and dictionary learning based on the MDL principle
The power of sparse signal coding with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical infe...
Ignacio Ramírez, Guillermo Sapiro
ILP
2007
Springer
16 years 14 days ago
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates
Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). W...
Mark Goadrich, Jude W. Shavlik
JMLR
2010
116views more  JMLR 2010»
15 years 1 months ago
Feature Selection, Association Rules Network and Theory Building
As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be us...
Sanjay Chawla
EACL
2003
ACL Anthology
15 years 7 months ago
Empirical Methods for Compound Splitting
Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We eva...
Philipp Koehn, Kevin Knight