Results 231 to 240 of about 8,828,165 (258)
STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling. [PDF]
Liu J +5 more
europepmc +1 more source
Guest editorial: Total least squares and errors-in-variables modeling
Van Huffel, S. +3 more
core +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Kernel-based Nonlinear Fit with Total Least Square(TLS) Method
2007 Chinese Control Conference, 2006In this paper, on the basis of linear fit in the total least square(TLS) method sense, we proposed a method of nonlinear fit in the TLS method sense via kernel representation. Namely, by using an appropriate kernel function, the problems of nonlinear fit can be transformed to the problems of linear fit without paying the computational penalty and ...
Hu Guanghua, Fu Guanghui
openaire +1 more source
Application of total least squares (TLS) to the design of sparse signal representation dictionaries
Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002., 2003Sparse signal representation has been the subject of much research in recent years in a variety of applications. We address the problem of learning a dictionary of waveforms from a given set of data signals, which may then be used to provide efficient and meaningful signal decompositions.
S.F. Cotter, B.D. Rao
openaire +1 more source
2005
We present a robust recursive total least squares (RRTLS) algorithm for multilayer feed-forward neural networks. So far, recursive least squares (RLS) has been successfully applied to training multilayer feed-forward neural networks. However, if input data has additive noise, the results from RLS could be biased.
Jun-Seok Lim, Nakjin Choi, Koeng-Mo Sung
openaire +2 more sources
We present a robust recursive total least squares (RRTLS) algorithm for multilayer feed-forward neural networks. So far, recursive least squares (RLS) has been successfully applied to training multilayer feed-forward neural networks. However, if input data has additive noise, the results from RLS could be biased.
Jun-Seok Lim, Nakjin Choi, Koeng-Mo Sung
openaire +2 more sources
A Recursive Restricted Total Least-Squares Algorithm [PDF]
International audienceWe show that the generalized total least squares (GTLS) problem with a singular noise covariance matrix is equivalent to the restricted total least squares (RTLS) problem and propose a recursive method for its numerical solution ...
Ivan Markovsky +2 more
exaly +2 more sources
The International Conference on Electrical Engineering, 2008
K. El-Barbary +3 more
openaire +1 more source
K. El-Barbary +3 more
openaire +1 more source
This study seeks to conduct an empirical evaluation of the performances of two soft computing methodologies comprising the Levenberg-Marquardt Back Propagation Artificial Neural Network (LMBPANN) and the Bayesian Regularisation Backpropagation Artificial Neural Network (BRBPANN).
Larbi, Edwin Kojo +2 more
openaire +1 more source
Larbi, Edwin Kojo +2 more
openaire +1 more source

