Advances in neural information processing systems 2
Neural Computation
Choice of Basis for Laplace Approximation
Machine Learning
Comparison of approximate methods for handling hyperparameters
Neural Computation
Generalization in Neural Networks and Machine Learning
Generalization in Neural Networks and Machine Learning
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
IEEE Transactions on Evolutionary Computation
Computers & Mathematics with Applications
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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An algorithm of the inductive model generation and model selection is proposed to solve the problem of automatic construction of regression models. A regression model is an admissible superposition of smooth functions given by experts. Coherent Bayesian inference is used to estimate model parameters. It introduces hyperparameters which describe the distribution function of the model parameters. The hyperparameters control the model generation process.