Zobrazeno 1 - 10
of 44
pro vyhledávání: '"adaptation of hyper-parameters"'
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meanin
Externí odkaz:
http://arxiv.org/abs/1406.2623
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Publikováno v:
Parallel Problem Solving from Nature – PPSN XIII ISBN: 9783319107615
PPSN
PPSN
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meanin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ace7cac6c580cea5c380f8d6e69d4c5
https://doi.org/10.1007/978-3-319-10762-2_7
https://doi.org/10.1007/978-3-319-10762-2_7
Publikováno v:
Scopus-Elsevier
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014)
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Sep 2014, Ljubljana, Slovenia. pp.70-79
CIÊNCIAVITAE
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014)
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Sep 2014, Ljubljana, Slovenia. pp.70-79
CIÊNCIAVITAE
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meanin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60ea4204107fe9514beb05faf38a3b0f
http://www.scopus.com/inward/record.url?eid=2-s2.0-84921849971&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-84921849971&partnerID=MN8TOARS
Autor:
Peng, Zemiao, Wu, Hao
A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy. However, existing NLFT models' objective functio
Externí odkaz:
http://arxiv.org/abs/2208.06778
Autor:
Chen, Jiufang, Yuan, Ye
Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make a
Externí odkaz:
http://arxiv.org/abs/2208.02423
Publikováno v:
International Journal of Computer Assisted Radiology & Surgery; May2016, Vol. 11 Issue 5, p777-788, 12p
Publikováno v:
PeerJ Computer Science; Oct2023, p1-26, 26p
Autor:
Loshchilov, Ilya1 ilya.loshchilov@gmail.com
Publikováno v:
Evolutionary Computation. Spring2017, Vol. 25 Issue 1, p143-171. 29p.
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. Methods: This method has an advantage over typic
Externí odkaz:
http://arxiv.org/abs/1510.01344