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» A distributed machine learning framework
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KDD
2002
ACM
147views Data Mining» more  KDD 2002»
16 years 6 months ago
Sequential cost-sensitive decision making with reinforcement learning
Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have ...
Edwin P. D. Pednault, Naoki Abe, Bianca Zadrozny
MM
2005
ACM
171views Multimedia» more  MM 2005»
15 years 12 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
CORR
2011
Springer
178views Education» more  CORR 2011»
14 years 10 months ago
Online Learning: Stochastic and Constrained Adversaries
Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and...
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
ICDE
2011
IEEE
200views Database» more  ICDE 2011»
14 years 10 months ago
Deriving probabilistic databases with inference ensembles
— Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assum...
Julia Stoyanovich, Susan B. Davidson, Tova Milo, V...
IPPS
2009
IEEE
16 years 29 days ago
Ibis: Real-world problem solving using real-world grids
Ibis is an open source software framework that drastically simplifies the process of programming and deploying large-scale parallel and distributed grid applications. Ibis suppor...
Henri E. Bal, Niels Drost, Roelof Kemp, Jason Maas...