Pages that link to "Item:Q2489152"

The following pages link to Approximation by neural networks and learning theory (Q2489152):

Displaying 46 items.

• Approximation by max-product neural network operators of Kantorovich type (Q287317) (← links)
• Model reduction by CPOD and Kriging: application to the shape optimization of an intake port (Q381471) (← links)
• Approximation by neural networks with weights varying on a finite set of directions (Q663685) (← links)
• Interpolation and rates of convergence for a class of neural networks (Q840184) (← links)
• Constrained proper orthogonal decomposition based on QR-factorization for aerodynamical shape optimization (Q907544) (← links)
• Application of adjoint operators to neural learning (Q917462) (← links)
• Local approximation on artificial neural networks (Q1285315) (← links)
• Approximation theorems for a family of multivariate neural network operators in Orlicz-type spaces (Q1623021) (← links)
• Neural network operators: constructive interpolation of multivariate functions (Q1669090) (← links)
• Saturation classes for MAX-product neural network operators activated by sigmoidal functions (Q1682591) (← links)
• Approximation results in Orlicz spaces for sequences of Kantorovich MAX-product neural network operators (Q1743222) (← links)
• Some problems in the theory of ridge functions (Q1798075) (← links)
• Critical points for least-squares problems involving certain analytic functions, with applications to sigmoidal nets (Q1923891) (← links)
• Approximation rates for neural networks with general activation functions (Q1982446) (← links)
• Approximation spaces of deep neural networks (Q2117336) (← links)
• Voronovskaja type theorems and high-order convergence neural network operators with sigmoidal functions (Q2178844) (← links)
• Almost optimal estimates for approximation and learning by radial basis function networks (Q2251472) (← links)
• Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation (Q2418176) (← links)
• Pointwise and uniform approximation by multivariate neural network operators of the max-product type (Q2418225) (← links)
• Approximation methods for supervised learning (Q2433154) (← links)
• On approximate learning by multi-layered feedforward circuits (Q2581366) (← links)
• Max-product neural network and quasi-interpolation operators activated by sigmoidal functions (Q2630379) (← links)
• Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos (Q2687674) (← links)
• Convergence for a family of neural network operators in Orlicz spaces (Q2965304) (← links)
• (Q3023437) (← links)
• (Q3093207) (← links)
• Learning Theory (Q3426914) (← links)
• Applications of learning theorems (Q3989949) (← links)
• (Q4220209) (← links)
• (Q4289869) (← links)
• (Q4365433) (← links)
• (Q4521302) (← links)
• Convergence results for a family of Kantorovich max-product neural network operators in a multivariate setting (Q4599380) (← links)
• (Q4892198) (← links)
• THE NEWTON NEURAL NET: A NEW APPROXIMATING NETWORK (Q4917772) (← links)
• (Q4938227) (← links)
• Deep Neural Network Approximation Theory (Q5001568) (← links)
• Asymptotics of Reinforcement Learning with Neural Networks (Q5084496) (← links)
• Quantitative estimates involving <i>K</i>-functionals for neural network-type operators (Q5197961) (← links)
• Approximation by sums of ridge functions with fixed directions (Q5369335) (← links)
• Geometric Rates of Approximation by Neural Networks (Q5448680) (← links)
• Using Prior Information to Improve the Approximation Performances of Neural Networks (Q5504342) (← links)
• Approximating smooth and sparse functions by deep neural networks: optimal approximation rates and saturation (Q6062170) (← links)
• Learning sparse and smooth functions by deep sigmoid nets (Q6109261) (← links)
• Approximation of classifiers by deep perceptron networks (Q6488832) (← links)
• Learning and approximating piecewise smooth functions by deep sigmoid neural networks (Q6634146) (← links)