Backward elimination model construction for regression and classification using leave-one-out criteria
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Publication:3437481
DOI10.1080/00207720601051463zbMath1129.93052OpenAlexW1997986924WikidataQ126268066 ScholiaQ126268066MaRDI QIDQ3437481
Publication date: 9 May 2007
Published in: International Journal of Systems Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207720601051463
Learning and adaptive systems in artificial intelligence (68T05) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)
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Uses Software
Cites Work
- Unnamed Item
- Least angle regression. (With discussion)
- Atomic Decomposition by Basis Pursuit
- Adaptive Modelling, Estimation and Fusion from Data
- Orthogonal least squares methods and their application to non-linear system identification
- 10.1162/15324430152748236
- Givens rotation based fast backward elimination algorithm for RBF neural network pruning
- The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
- The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
- Backward Elimination Methods for Associative Memory Network Pruning
- Soft margins for AdaBoost