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» Evaluating algorithms that learn from data streams
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DAWAK
2010
Springer
15 years 1 months ago
Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...
Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
98
Voted
ICML
2008
IEEE
16 years 1 months ago
Active kernel learning
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A variety of kernel learning algorithms have been prop...
Steven C. H. Hoi, Rong Jin
COLT
2005
Springer
15 years 6 months ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
183
Voted
SIGMOD
2008
ACM
125views Database» more  SIGMOD 2008»
16 years 22 days ago
Categorical skylines for streaming data
The problem of skyline computation has attracted considerable research attention. In the categorical domain the problem becomes more complicated, primarily due to the partially-or...
Nikos Sarkas, Gautam Das, Nick Koudas, Anthony K. ...
AIPRF
2007
15 years 2 months ago
Evaluation of Different Approaches to Training a Genre Classifier
This paper presents experiments on classifying web pages by genre. Firstly, a corpus of 1539 manually labeled web pages was prepared. Secondly, 502 genre features were selected ba...
Vedrana Vidulin, Mitja Lustrek, Matjaz Gams