PRL
2007
13 years 5 months ago
2007
Abstract – In this paper we seek a Gaussian mixture model (GMM) of the classconditional densities for plug-in Bayes classification. We propose a method for setting the number of ...
SP
2002
IEEE
13 years 5 months ago
2002
IEEE
Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive stat...
ML
2002
ACM
13 years 5 months ago
2002
ACM
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitt...
MCS
2002
Springer
13 years 5 months ago
2002
Springer
Abstract. We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm det...
ML
2000
ACM
13 years 6 months ago
2000
ACM
We present an approach to inductive concept learning using multiple models for time series. Our objective is to improve the efficiency and accuracy of concept learning by decomposi...
TSMC
2008
13 years 6 months ago
2008
Abstract--Practical applications call for efficient model selection criteria for multiclass support vector machine (SVM) classification. To solve this problem, this paper develops ...
SYNTHESE
2008
13 years 6 months ago
2008
Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodne...
IEICET
2007
13 years 6 months ago
2007
In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the gener...
IEICET
2007
13 years 6 months ago
2007
Estimating the generalization error is one of the key ingredients of supervised learning since a good generalization error estimator can be used for model selection. An unbiased g...
CSDA
2007
13 years 6 months ago
2007
The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodol...