Journal Article10.1016/0925-2312(95)00121-2
Variable selection with neural networks
100
TL;DR: 3 different neural network-based methods to perform variable selection using two priors (a Gaussian and a Gaussian mixture) are presented and it is shown that this regularization approach allows to select efficient subsets of variables.
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About: This article is published in Neurocomputing. The article was published on 31 Jul 1996. The article focuses on the topics: Feature selection & Prior probability.
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Citations
Neural networks for classification: a survey
G.P. Zhang
- 01 Nov 2000
TL;DR: The issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined.
Subset Selection in Regression
TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
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A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification
TL;DR: Very high-resolution panchromatic images from QuickBird and WorldView-1 have been used to accurately classify the land-use of four different urban environments and show that with a multi-scale approach it is possible to discriminate different asphalt surfaces due to the different textural information content.
394
A new pruning heuristic based on variance analysis of sensitivity information
TL;DR: A new pruning algorithm is presented that uses the sensitivity analysis to quantify the relevance of input and hidden units and a new statistical pruning heuristic is proposed, based on the variance analysis, to decide which units to prune.
230
•Journal Article
Feature Subset Selection Using Ant Colony Optimization
TL;DR: A novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented, a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources.
References
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
Gideon Schwarz
- 01 Jan 2005
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
40.6K
•Book
Classification and regression trees
Leo Breiman
- 01 Jan 1983
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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