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» Maximum likelihood analysis of algorithms and data structure...
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ML
2008
ACM
222views Machine Learning» more  ML 2008»
13 years 7 months ago
Boosted Bayesian network classifiers
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...
Yushi Jing, Vladimir Pavlovic, James M. Rehg
CSDA
2011
13 years 2 months ago
Inferences on Weibull parameters with conventional type-I censoring
In this article we consider the statistical inferences of the unknown parameters of a Weibull distribution when the data are Type-I censored. It is well known that the maximum lik...
Avijit Joarder, Hare Krishna, Debasis Kundu
IBPRIA
2003
Springer
14 years 28 days ago
Incrementally Assessing Cluster Tendencies with a Maximum Variance Cluster Algorithm
A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the ...
Krzysztof Rzadca, Francesc J. Ferri
JMLR
2000
134views more  JMLR 2000»
13 years 7 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
Marina Meila, Michael I. Jordan
ISCAS
2003
IEEE
172views Hardware» more  ISCAS 2003»
14 years 1 months ago
Efficient symbol synchronization techniques using variable FIR or IIR interpolation filters
Maximum Likelihood estimation theory can be used to develop optimal timing recovery schemes for digital communication systems. Tunable digital interpolation filters are commonly ...
Martin Makundi, Timo I. Laakso