The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the ...
Benefits of Combining Circulating Tumor DNA With Tissue and Longitudinal Circulating Tumor DNA Genotyping in Advanced Solid Tumors: SCRUM-Japan MONSTAR-SCREEN-1 Study Osteosarcoma (OS) is the most ...
Abstract: The brief proposes a radial basis function (RBF) neural network (NN)-enabled adaptive filter (AF) algorithm, which consists of two stages. The first stage is a data-driven (DD) preprocessing ...
Introduction: Intelligent vehicles and autonomous driving have been the focus of research in the field of transport, but current autonomous driving models have significant errors in lateral tracking ...
Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regression and classification ...
First at all, thanks a lot for putting this nice tool available for everyone. It is really nice! I have a question regarding which RBF basis to choose in case I am interested in obtaining smooth ...
Continual learning is a rapidly evolving area of research that focuses on developing models capable of learning from sequentially arriving data streams, similar to human learning. It addresses the ...
ABSTRACT: Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating ...
Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating higher order ...
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