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KDD
1997
ACM
109views Data Mining» more  KDD 1997»
14 years 2 months ago
Beyond Concise and Colorful: Learning Intelligible Rules
A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful ...
Michael J. Pazzani, Subramani Mani, William Rodman...
INFFUS
2008
97views more  INFFUS 2008»
13 years 10 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
ISCI
2007
130views more  ISCI 2007»
13 years 10 months ago
Computing with words and its relationships with fuzzistics
ct 9 Words mean different things to different people, and so are uncertain. We, therefore, need a fuzzy set model for a word 10 that has the potential to capture their uncertaint...
Jerry M. Mendel
IJCNN
2007
IEEE
14 years 4 months ago
An Associative Memory for Association Rule Mining
— Association Rule Mining is a thoroughly studied problem in Data Mining. Its solution has been aimed for by approaches based on different strategies involving, for instance, the...
Vicente O. Baez-Monroy, Simon O'Keefe
ML
2010
ACM
151views Machine Learning» more  ML 2010»
13 years 8 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales