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» A distributed machine learning framework
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CONCUR
2006
Springer
15 years 4 months ago
Minimization, Learning, and Conformance Testing of Boolean Programs
Boolean programs with recursion are convenient abstractions of sequential imperative programs, and can be represented as recursive state machines (RSMs) or pushdown automata. Motiv...
Viraj Kumar, P. Madhusudan, Mahesh Viswanathan
96
Voted
VMV
2008
107views Visualization» more  VMV 2008»
15 years 2 months ago
Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Erik Rodner, Joachim Denzler
114
Voted
HPDC
2005
IEEE
15 years 6 months ago
Increasing application performance in virtual environments through run-time inference and adaptation
Virtual machine distributed computing greatly simplifies the use of widespread computing resources by lowering the abstraction, benefiting both resource providers and users. Tow...
Ananth I. Sundararaj, Ashish Gupta, Peter A. Dinda
102
Voted
IPPS
2008
IEEE
15 years 7 months ago
CoSL: A coordinated statistical learning approach to measuring the capacity of multi-tier websites
Website capacity determination is crucial to measurement-based access control, because it determines when to turn away excessive client requests to guarantee consistent service qu...
Jia Rao, Cheng-Zhong Xu
94
Voted
NIPS
2004
15 years 2 months ago
Semi-supervised Learning by Entropy Minimization
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
Yves Grandvalet, Yoshua Bengio