Artificial neural networks with an infinite number of nodes
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[1] Nikolas P. Galatsanos,et al. Sparse Bayesian Modeling With Adaptive Kernel Learning , 2009, IEEE Transactions on Neural Networks.
[2] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[3] Zhe George Zhang,et al. Forecasting stock indices with back propagation neural network , 2011, Expert Syst. Appl..
[4] Florian Steinke,et al. Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.
[5] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[6] Isaac E. Lagaris,et al. Stopping rules for box-constrained stochastic global optimization , 2008, Appl. Math. Comput..
[7] Kurt Hornik,et al. The support vector machine under test , 2003, Neurocomputing.
[8] R. Fletcher,et al. A New Approach to Variable Metric Algorithms , 1970, Comput. J..
[9] . Andersont,et al. Mathematics of Control , Signals , and Systems , 2017 .
[10] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[11] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[12] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[13] Jocelyn Sietsma,et al. Creating artificial neural networks that generalize , 1991, Neural Networks.
[14] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[15] Michael E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..
[16] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[17] Peter L. Bartlett,et al. The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.
[18] Dimitris G. Papageorgiou,et al. Neural-network methods for boundary value problems with irregular boundaries , 2000, IEEE Trans. Neural Networks Learn. Syst..
[19] D. Fotiadis,et al. Artificial neural network methods in quantum mechanics , 1997, quant-ph/9705029.
[20] J. G. Hayes. NAG algorithms for the approximation of functions and data , 1987 .
[21] D. Broomhead,et al. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .
[22] Konstantinos Blekas,et al. Under Consideration for Publication in Knowledge and Information Systems Sparse Regression Mixture Modeling with the Multi-kernel Relevance Vector Machine , 2022 .
[23] Dimitrios I. Fotiadis,et al. Artificial neural networks for solving ordinary and partial differential equations , 1997, IEEE Trans. Neural Networks.