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» A distributed machine learning framework
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KDD
2002
ACM
147views Data Mining» more  KDD 2002»
14 years 11 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»
14 years 4 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»
13 years 2 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»
13 years 2 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
14 years 5 months 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...