Results 201 to 210 of about 327,869 (262)

From linear models to deep learning: statistical advances in genomic selection for animal breeding. [PDF]

J Anim Sci Biotechnol
Zhang L, Zhang M, Wang X, Chen C, Wang S, Zhao Y, Zhang Y, Wang R, Liu Y.   +8 more
europepmc   +1 more source

Application of machine learning in predicting of MB2 canal in permanent maxillary first and second molars: A CBCT study. [PDF]

PLoS One
Talabani RM.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Optimization on parametric model

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, 2018
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with large number of weights consume considerable storage and memory bandwidth. To address this limitation, prun­ing is an effective way to compress neural networks with high accuracy.
Fenfen Huang, Wenbin Yao
openaire   +1 more source

Neuro-wavelet parametric modeling

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
This work describes Neuro-Wavelet Parametric Modeling, a neural-based technique to classify, model and forecast signals or problems which are functions of either time or space. The paper presents the base method and discusses on the selection of the optimal neuro-wavelet network. An industrial application is also presented.
COLLA, Valentina, L. M. Reyneri, SGARBI, MIRKO   +2 more
openaire   +4 more sources

Identification in Parametric Models

Econometrica, 1971
A theory of identification is developed for a general stochastic model whose probability law is determined by a finite number of parameters. It is shown under weak regularity conditions that local identifiability of the unknown parameter vector is equivalent to nonsingularity of the information matrix.
openaire   +2 more sources

A parametric yield model

Journal of Electronic Testing, 1995
Assuming that the distribution of path delays introduced by variations in the manufacturing process is exponential instead of gaussian, the interdependence problem between delay-optimization of synthesized networks and parametric yield has been revisited. The result confirms the claim of Williams, Underwood, and Mercer.
openaire   +2 more sources

Parametric eye models.

Studies in health technology and informatics, 2007
The shape of anatomic objects often depends in complex ways on the shapes and locations of neighboring objects. Shape parameter networks provide an approach for representing shape dependencies and producing multi-object models that share consistent boundary definitions.
Jessica R. Crouch, Andrew Cherry
openaire   +2 more sources