Sequential function approximation with noisy data

2018 ◽  
Vol 371 ◽  
pp. 363-381 ◽  
Author(s):  
Yeonjong Shin ◽  
Kailiang Wu ◽  
Dongbin Xiu
Keyword(s):  
Function Approximation ◽  
Noisy Data ◽  
Sequential Function Approximation

Function approximation from noisy data by an incremental RBF network

1999 ◽  
Vol 32 (12) ◽  
pp. 2081-2083 ◽  
Author(s):  
Menita Carozza ◽  
Salvatore Rampone
Keyword(s):  
Function Approximation ◽  
Noisy Data ◽  
Rbf Network

A SPARSE GREEDY SELF-ADAPTIVE ALGORITHM FOR CLASSIFICATION OF DATA

2010 ◽  
Vol 02 (01) ◽  
pp. 97-114 ◽  
Author(s):  
ANKUR SRIVASTAVA ◽  
ANDREW J. MEADE
Keyword(s):  
Function Approximation ◽  
Classification Algorithms ◽  
Radial Basis Function Networks ◽  
Data Sets ◽  
Grid Search ◽  
Real World Data ◽  
Adaptive Technique ◽  
Comparable Performance ◽  
Sequential Function Approximation

Kernels have become an integral part of most data classification algorithms. However, the kernel parameters are generally not optimized during learning. In this work a novel adaptive technique called Sequential Function Approximation (SFA) has been developed for classification that determines the values of the control and kernel hyper-parameters during learning. This tool constructs sparse radial basis function networks in a greedy fashion. Experiments were carried out on synthetic and real-world data sets where SFA had comparable performance to other popular classification schemes with parameters optimized by an exhaustive grid search.

Noisy Data: Incorporating Variability into Your Analysis

2014 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Richard Schwartz
Keyword(s):  
Noisy Data
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