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» Genetic Process Mining: A Basic Approach and Its Challenges
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SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 8 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
GRC
2010
IEEE
13 years 9 months ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
ALT
2004
Springer
14 years 5 months ago
Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space
Different formal learning models address different aspects of learning. Below we compare learning via queries—interpreting learning as a one-shot process in which the learner i...
Steffen Lange, Sandra Zilles
BMCBI
2005
87views more  BMCBI 2005»
13 years 8 months ago
Efficient decoding algorithms for generalized hidden Markov model gene finders
Background: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility an...
William H. Majoros, Mihaela Pertea, Arthur L. Delc...
SIGIR
2005
ACM
14 years 2 months ago
Detecting dominant locations from search queries
Accurately and effectively detecting the locations where search queries are truly about has huge potential impact on increasing search relevance. In this paper, we define a search...
Lee Wang, Chuang Wang, Xing Xie, Josh Forman, Yans...