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» Optimizing Frequency Queries for Data Mining Applications
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KDD
2004
ACM
124views Data Mining» more  KDD 2004»
14 years 1 months ago
Incorporating prior knowledge with weighted margin support vector machines
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus ...
Xiaoyun Wu, Rohini K. Srihari
PAKDD
2010
ACM
198views Data Mining» more  PAKDD 2010»
14 years 18 days ago
A Better Strategy of Discovering Link-Pattern Based Communities by Classical Clustering Methods
Abstract. The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of linkpattern based community was pr...
Chen-Yi Lin, Jia-Ling Koh, Arbee L. P. Chen
ISCI
2008
116views more  ISCI 2008»
13 years 7 months ago
Discovery of maximum length frequent itemsets
The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often ...
Tianming Hu, Sam Yuan Sung, Hui Xiong, Qian Fu
ALT
2009
Springer
14 years 4 months ago
Average-Case Active Learning with Costs
Abstract. We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have differe...
Andrew Guillory, Jeff A. Bilmes
KDD
2009
ACM
152views Data Mining» more  KDD 2009»
14 years 8 months ago
TANGENT: a novel, 'Surprise me', recommendation algorithm
Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored ...
Kensuke Onuma, Hanghang Tong, Christos Faloutsos