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» Quantity Makes Quality: Learning with Partial Views
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KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
ASIACRYPT
2011
Springer
12 years 7 months ago
The Leakage-Resilience Limit of a Computational Problem Is Equal to Its Unpredictability Entropy
A cryptographic assumption is the (unproven) mathematical statement that a certain computational problem (e.g. factoring integers) is computationally hard. The leakage-resilience l...
Divesh Aggarwal, Ueli Maurer
NIPS
2004
13 years 9 months ago
Confidence Intervals for the Area Under the ROC Curve
In many applications, good ranking is a highly desirable performance for a classifier. The criterion commonly used to measure the ranking quality of a classification algorithm is ...
Corinna Cortes, Mehryar Mohri
PDP
2002
IEEE
14 years 19 days ago
A Jini Framework for Distributed Service Flexibility
Existing distributed middleware technologies and Enterprise Application frameworks lack in support to service flexibility from both the developer’s and user’s point of view. ...
Domenico Cotroneo, Cristiano di Flora, Stefano Rus...
ACL
2006
13 years 9 months ago
Modelling Lexical Redundancy for Machine Translation
Certain distinctions made in the lexicon of one language may be redundant when translating into another language. We quantify redundancy among source types by the similarity of th...
David Talbot, Miles Osborne