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» Mining Multiple Large Databases
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137
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KDD
2005
ACM
161views Data Mining» more  KDD 2005»
16 years 3 months ago
Combining email models for false positive reduction
Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolk...
Shlomo Hershkop, Salvatore J. Stolfo
123
Voted
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
15 years 8 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
125
Voted
PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
15 years 1 months ago
Entropy and Margin Maximization for Structured Output Learning
Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
121
Voted
SSD
2007
Springer
133views Database» more  SSD 2007»
15 years 8 months ago
Compression of Digital Road Networks
Abstract. In the consumer market, there has been an increasing interest in portable navigation systems in the last few years. These systems usually work on digital map databases st...
Jonghyun Suh, Sungwon Jung, Martin Pfeifle, Khoa T...
146
Voted
AVI
2008
15 years 3 months ago
Query-through-drilldown: data-oriented extensional queries
Traditional database query formulation is intensional: at the level of schemas, table and column names. Previous work has shown that filters can be created using a query paradigm ...
Alan J. Dix, Damon Oram