The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis Fault Breccia: Regression and artificial neural networks analysis

2010 ◽  
Vol 37 (12) ◽  
pp. 8750-8756 ◽  
Author(s):  
S. Kahraman ◽  
M. Alber ◽  
M. Fener ◽  
O. Gunaydin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cerchar Abrasivity Index ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Fault Breccia

scholarly journals Computational Principles of Supervised Learning in the Cerebellum

2018 ◽  
Vol 41 (1) ◽  
pp. 233-253 ◽  
Author(s):  
Jennifer L. Raymond ◽  
Javier F. Medina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Brain Area ◽  
Future Research ◽  
Brain Areas ◽  
Adaptive Mechanisms ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Discovering gastronomic tourists’ profiles through artificial neural networks: analysis, opinions and attitudes

2021 ◽  
pp. 1-12
Author(s):  
Salvador Moral-Cuadra ◽  
Miguel Á. Solano-Sánchez ◽  
Antonio Menor-Campos ◽  
Tomás López-Guzmán
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Networks Analysis ◽  
Artificial Neural

scholarly journals Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e27277 ◽  
Author(s):  
Cristina Eller-Vainicher ◽  
Iacopo Chiodini ◽  
Ivana Santi ◽  
Marco Massarotti ◽  
Luca Pietrogrande ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vertebral Fractures ◽  
Networks Analysis ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document