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Function approximation
Kriging Prediction with Isotropic Matérn Correlations: Robustness
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
Rational Function Approximation
Piecewise Linear Approximation of Streaming Time Series Data with Max-Error Guarantees
Rethinking Statistical Learning Theory: Learning Using Statistical Invariants
Function Approximation with Mlps, Radial Basis Functions, and Support Vector Machines
Uhm Ms 3812 R.Pdf
Rational Function Approximation
Simplified Neural Networks Algorithms for Function Approximation and Regression
Time Series Compression: a Survey
Deep Neural Network Approximation Theory
Modeling Periodic Functions for Time Series Analysis
Smooth Function Approximation Using Neural Networks Silvia Ferrari, Member, IEEE, and Robert F
Approximation Theory Function Approximation Is the Task Of
Function Approximation
Kriging and Splines, and Their Application to the Characterization of Near-Circular Features Hae-Il Ahn Iowa State University
A Brief Survey of Parametric Value Function Approximation
Neurosolutions for Excel
Top View
Finite Element Function Approximation 1 Piecewise Linear Interpolation
Smooth Function Approximation by Deep Neural Networks with General Activation Functions
Chapter 5 Function Approximation
Rule Driven Job-Shop Scheduling Derived From
An Efficient Algorithm for Kriging Approximation and Optimization
Learning Correlation Space for Time Series
Interpolation, Approximation and Their Applications
Lecture Notes by Karen Kopecky at • – Tauchen (86) Method – Tauchen and Hussey (91) Method – Rouwenhorst (95) Method
Approximation Methods
Design and Analysis of Learning Classifier Systems: a Probabilistic
Feature Selection for Value Function Approximation
Function Approximation
Function Approximation Quantitative Macroeconomics
Low-Rank Methods
A `Best Points' Interpolation Method for Efficient Approximation Of