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» Learning and Generalization with the Information Bottleneck
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VLDB
2001
ACM
190views Database» more  VLDB 2001»
15 years 11 months ago
LEO - DB2's LEarning Optimizer
Most modern DBMS optimizers rely upon a cost model to choose the best query execution plan (QEP) for any given query. Cost estimates are heavily dependent upon the optimizer’s e...
Michael Stillger, Guy M. Lohman, Volker Markl, Mok...
CN
2006
163views more  CN 2006»
15 years 6 months ago
A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation
The expanding and dynamic nature of the Web poses enormous challenges to most data mining techniques that try to extract patterns from Web data, such as Web usage and Web content....
Olfa Nasraoui, Carlos Rojas, Cesar Cardona
CVIU
2007
137views more  CVIU 2007»
15 years 6 months ago
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Facial expression recognition is an active research area that finds a potential application in human emotion analysis. This work presents an illumination independent approach for...
Benjamín Hernández, Gustavo Olague, ...
ACMIDC
2011
14 years 10 months ago
LightUp: a low-cost, multi-age toolkit for learning and prototyping electronics
LightUp is a constructionist platform to teach novices about electronics, and also a low-cost rapid-prototyping platform for more advanced users. The LightUp kit contains many bas...
Zain Asgar, Joshua Chan, Chang Liu, Paulo Blikstei...
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
16 years 7 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen