Results 181 to 190 of about 2,461 (211)

Features predicting data exclusion in imaging studies of Alzheimer's disease. [PDF]

Alzheimers Dement (Amst)
Flores S, Scott J, Lu R, Su Y, Keefe SJ, Smith TH, Hornbeck RC, McKay NS, Strain JF, Simmons A, Dombrowski K, Rizzo J, Shimony H, Bateman RJ, Babulal GM, Landau SM, Jagust W, Wolk DA, Lah J, Gleason C, Johnson S, Roberson ED, Sperling R, Johnson K, Morris JC, Xiong C, Gordon BA, Benzinger TLS.   +27 more
europepmc   +1 more source

Melanoma cell states shape spatial tumor-immune ecosystems to dictate the efficacy of anti-PD1 immunotherapy. [PDF]

J Immunother Cancer
Pham FK, Dufeu M, Benboubker V, Grimont M, Lhorisson A, Berthet J, Donzel M, Schneider R, Tonon L, Doffin AC, Boivin F, Durand S, Dubois B, Lopez J, Caux C, Valladeau-Guilemond J, Eberhardt A, Dalle S, Caramel J.   +18 more
europepmc   +1 more source

Thermal Conductivity in Mortar Samples with Copper Mine Tailings. [PDF]

Materials (Basel)
Daza L, Gómez R, Díaz-Noriega R, Gómez-Espina R, Skrzypkowski K, Jerez O.   +5 more
europepmc   +1 more source

Cryo-EM structures of Egl–BicD–RNA complexes reveal how diverse mRNAs are selected for subcellular localization

Singh K, Chilaeva S, McClintock MA, Carter AP, Bullock SL.   +4 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Kernel-based Nonlinear Fit with Total Least Square(TLS) Method

2007 Chinese Control Conference, 2006
In 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., 2003
Sparse 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

Robust Recursive TLS (Total Least Square) Method Using Regularized UDU Decomposed for FNN (Feedforward Neural Network) Training

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   +1 more source

Adjustment of Real-time Kinematic-Global Positioning System (RTK-GPS) Survey Data: A Comparative Performance Analysis of the Back Propagation Artificial Neural Network (BPANN) and the Total Least Squares (TLS) Techniques

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, Peprah, Michael Stanley, Asenso-gyambibi, Daniel   +2 more
openaire   +1 more source

<title>Total least squares (TLS) regression, wavelength, and chromophore selection analysis in NIRS data quantification</title>

SPIE Proceedings, 1995
Sabine Van Huffel, Paul Casaer, Peter Van Mele, Gert Willems   +3 more
openaire   +1 more source