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» Using Machine Learning to Focus Iterative Optimization
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129
Voted
FAST
2009
15 years 16 days ago
DIADS: Addressing the "My-Problem-or-Yours" Syndrome with Integrated SAN and Database Diagnosis
We present DIADS, an integrated DIAgnosis tool for Databases and Storage area networks (SANs). Existing diagnosis tools in this domain have a database-only (e.g., [11]) or SAN-onl...
Shivnath Babu, Nedyalko Borisov, Sandeep Uttamchan...
ICML
2009
IEEE
16 years 3 months ago
Robust bounds for classification via selective sampling
We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...
Nicolò Cesa-Bianchi, Claudio Gentile, Franc...
114
Voted
ECML
2007
Springer
15 years 9 months ago
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
Abstract. The paper investigates modification of backpropagation algorithm, consisting of discretization of neural network weights after each training cycle. This modification, a...
Marcin Wojnarski
173
Voted
ECCV
2006
Springer
16 years 4 months ago
Example Based Non-rigid Shape Detection
Since it is hard to handcraft the prior knowledge in a shape detection framework, machine learning methods are preferred to exploit the expert annotation of the target shape in a d...
Yefeng Zheng, Xiang Sean Zhou, Bogdan Georgescu, S...
110
Voted
ICML
2004
IEEE
16 years 3 months ago
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung