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ICML
2005
IEEE
14 years 11 months ago
Augmenting naive Bayes for ranking
Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is m...
Harry Zhang, Liangxiao Jiang, Jiang Su
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 10 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
KDD
2006
ACM
159views Data Mining» more  KDD 2006»
14 years 10 months ago
Global distance-based segmentation of trajectories
This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primi...
Aris Anagnostopoulos, Michail Vlachos, Marios Hadj...
KDD
2001
ACM
203views Data Mining» more  KDD 2001»
14 years 10 months ago
Ensemble-index: a new approach to indexing large databases
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
Eamonn J. Keogh, Selina Chu, Michael J. Pazzani
VLDB
2005
ACM
109views Database» more  VLDB 2005»
14 years 10 months ago
Model-based approximate querying in sensor networks
Abstract Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" i...
Amol Deshpande, Carlos Guestrin, Samuel Madden, Jo...