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NIPS
2003
15 years 6 months ago
Kernel Dimensionality Reduction for Supervised Learning
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
CORR
2007
Springer
112views Education» more  CORR 2007»
15 years 5 months ago
Learning from compressed observations
— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
Maxim Raginsky
IJON
2002
105views more  IJON 2002»
15 years 4 months ago
Separation of sources using simulated annealing and competitive learning
This paper presents a new adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated a...
Carlos García Puntonet, Ali Mansour, Christ...
153
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JMLR
2010
153views more  JMLR 2010»
14 years 12 months ago
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits m...
Gideon S. Mann, Andrew McCallum
ISBI
2008
IEEE
16 years 5 months ago
Distributed online anomaly detection in high-content screening
This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive be...
Adam Goode, Rahul Sukthankar, Lily B. Mummert, Mei...