Results 231 to 240 of about 2,165,044 (267)
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IEEE Transactions on Systems, Man, and Cybernetics, 1995
Complex systems such as computers, aerospace systems, etc., are often tested by using a sequence of tests to exercise the functionality of the system. If the system fails a test, an error message is generated, initiating the test selection (TS) phase. The troubleshooter must decide whether or not to run more tests.
Inderpal S. Bhandari +2 more
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Complex systems such as computers, aerospace systems, etc., are often tested by using a sequence of tests to exercise the functionality of the system. If the system fails a test, an error message is generated, initiating the test selection (TS) phase. The troubleshooter must decide whether or not to run more tests.
Inderpal S. Bhandari +2 more
openaire +1 more source
1994
Fitting data by a certain generalized linear model means choosing appropriate forms for the predictor, the link function, and the exponential family or variance function. In the previous chapters Pearsons’s X 2, the deviance and, in the multinomial case, the power-divergence family were introduced as general goodness-of-fit statistics.
Ludwig Fahrmeir, Gerhard Tutz
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Fitting data by a certain generalized linear model means choosing appropriate forms for the predictor, the link function, and the exponential family or variance function. In the previous chapters Pearsons’s X 2, the deviance and, in the multinomial case, the power-divergence family were introduced as general goodness-of-fit statistics.
Ludwig Fahrmeir, Gerhard Tutz
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Model Selection for the Trend Vector Model
Journal of Classification, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hsiu-Ting Yu, Mark de Rooij
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Comparison of Model Selection for Regression
Neural Computation, 2003We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods—Akaike information criterion (AIC) and Bayesian ...
Vladimir Cherkassky, Yunqian Ma
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Bayesian Model Selection and Model Averaging
Journal of Mathematical Psychology, 2000This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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A Selecting-the-Best Method for Budgeted Model Selection
2011The paper focuses on budgeted model selection, that is the selection between a set of alternative models when the ratio between the number of model assessments and the number of alternatives, though bigger than one, is low. We propose an approach based on the notion of probability of correct selection, a notion borrowed from the domain of Monte Carlo ...
Gianluca Bontempi, Olivier Caelen
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Model selection in acoustic modeling
6th European Conference on Speech Communication and Technology, 1999Scott Saobing Chen, Ramesh A. Gopinath
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Cross validation for model selection: A review with examples from ecology
Ecological Monographs, 2023Luke Yates +2 more
exaly