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» Practical Preference Relations for Large Data Sets
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CORR
2011
Springer
161views Education» more  CORR 2011»
14 years 9 months ago
Doubly Robust Policy Evaluation and Learning
We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as...
Miroslav Dudík, John Langford, Lihong Li
CVPR
2011
IEEE
14 years 9 months ago
Compact Hashing with Joint Optimization of Search Accuracy and Time
Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results dem...
Junfeng He, Regunathan Radhakrishnan, Shih-Fu Chan...
NMR
2004
Springer
15 years 10 months ago
Preferential defeasibility: utility in defeasible logic programming
The development of Logic Programming and Defeasible Argumentation lead to Defeasible Logic Programming. Its core resides in the characterization of the warrant procedure. Defeasib...
Fernando A. Tohmé, Guillermo Ricardo Simari
VLDB
2004
ACM
227views Database» more  VLDB 2004»
15 years 10 months ago
Approximate NN queries on Streams with Guaranteed Error/performance Bounds
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with...
Nick Koudas, Beng Chin Ooi, Kian-Lee Tan, Rui Zhan...
CIKM
2010
Springer
15 years 3 months ago
Automatic schema merging using mapping constraints among incomplete sources
Schema merging is the process of consolidating multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autono...
Xiang Li 0002, Christoph Quix, David Kensche, Sand...