The following pages link to (Q4365433):
Displaying 50 items.
- Data processing and feature screening in function approximation: An application to neural networks (Q597242) (← links)
- Quadrature formula for computed tomography (Q606680) (← links)
- Neural networks and the best trigomometric approximation (Q646772) (← links)
- Approximation by neural networks with weights varying on a finite set of directions (Q663685) (← links)
- Approximation by polynomials and ridge functions of classes of \(s\)-monotone radial functions (Q927688) (← links)
- A class \(+1\) sigmoidal activation functions for FFANNs (Q951450) (← links)
- Gaussian kernel approximation algorithm for feedforward neural network design (Q1044442) (← links)
- Harmonic analysis of neural networks (Q1283559) (← links)
- Local approximation on artificial neural networks (Q1285315) (← links)
- Mathematics and neural networks -- A glance at some basic connections (Q1288752) (← links)
- On best approximation by ridge functions (Q1300146) (← links)
- On the approximation of functional classes equipped with a uniform measure using ridge functions (Q1300147) (← links)
- Lower bounds for approximation by MLP neural networks (Q1305902) (← links)
- Davidon least squares-based learning algorithm for feedforward neural networks (Q1345266) (← links)
- AR parameter estimation by a feedback neural network (Q1391327) (← links)
- A better approximation for balls (Q1577916) (← links)
- An efficient hardware implementation of feed-forward neural networks (Q1768499) (← links)
- Some problems in the theory of ridge functions (Q1798075) (← links)
- On best approximation of classes by radial functions (Q1867260) (← links)
- Approximation properties of a multilayered feedforward artificial neural network (Q1895884) (← links)
- Chebyshev approximation by discrete superposition. Application to neural networks (Q1923884) (← links)
- Representation of polynomials by linear combinations of radial basis functions (Q1943997) (← links)
- Approximation with neural networks activated by ramp sigmoids (Q1958429) (← links)
- Holomorphic feedforward networks (Q2074854) (← links)
- Approximation spaces of deep neural networks (Q2117336) (← links)
- Robust and resource-efficient identification of two hidden layer neural networks (Q2117339) (← links)
- On the approximation by single hidden layer feedforward neural networks with fixed weights (Q2179313) (← links)
- The errors of approximation for feedforward neural networks in thelpmetric (Q2390185) (← links)
- Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation (Q2418176) (← links)
- Neural-network approximation of functions of several variables (Q2453379) (← links)
- Parameter redundancy in neural networks: an application of Chebyshev polynomials (Q2468329) (← links)
- Linear and nonlinear methods of relief approximation (Q2519277) (← links)
- Surface approximation using the 2d FFENN architecture (Q2570296) (← links)
- On approximate learning by multi-layered feedforward circuits (Q2581366) (← links)
- The Activity of K. I. Oskolkov in Nonlinear Approximation of Functions (Q2840640) (← links)
- The construction and approximation of a class of neural networks operators with Ramp functions (Q2912678) (← links)
- Empirical prediction limit estimation methods for feed-forward neural networks (Q3426358) (← links)
- Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers (Q4323349) (← links)
- Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives (Q4323351) (← links)
- (Q4521302) (← links)
- THE NEWTON NEURAL NET: A NEW APPROXIMATING NETWORK (Q4917772) (← links)
- (Q4938227) (← links)
- Optimal Approximation with Sparsely Connected Deep Neural Networks (Q5025773) (← links)
- Full error analysis for the training of deep neural networks (Q5083408) (← links)
- Design and approximation of SISO three layers feedforward neural network based on Bernstein polynomials (Q5196794) (← links)
- Approximation by sums of ridge functions with fixed directions (Q5369335) (← links)
- Using Prior Information to Improve the Approximation Performances of Neural Networks (Q5504342) (← links)
- Neural network approximation (Q5887830) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- Successive approximation training algorithm for feedforward neural networks (Q5958024) (← links)
