Results 21 to 30 of about 2,165,044 (267)

Source Model Selection for Deep Learning in the Time Series Domain

IEEE Access, 2020
Transfer Learning aims to transfer knowledge from a source task to a target task. We focus on a situation when there is a large number of available source models, and we are interested in choosing a single source model that can maximize the predictive ...
Amiel Meiseles, Lior Rokach
doaj   +1 more source

Models for chronology selection [PDF]

Physical Review D, 1998
20 pages ...
Cassidy, M. J., Hawking, S. W.
openaire   +2 more sources

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

Remote Sensing, 2018
Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality ...
Katalin Blix, Torbjørn Eltoft
doaj   +1 more source

Non Asymptotic Sharp Oracle Inequalities for the Improved Model Selection Procedures for the Adaptive Nonparametric Signal Estimation Problem

Communications, 2018
In this paper, we consider the robust adaptive non parametric estimation problem for the periodic function observed with the Levy noises in continuous time.
Evgeny Pchelintsev, Valeriy Pchelintsev, Serguei Pergamenshchikov   +2 more
doaj   +1 more source

Active Model Selection

CoRR, 2012
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
Omid Madani, Daniel J. Lizotte, Russell Greiner   +2 more
openaire   +3 more sources

COSMOLOGICAL MODEL SELECTION [PDF]

International Journal of Modern Physics A, 2008
We give an overview of the recent progress in the field of cosmological model selection. Model selection statistics, such as those based on information theory and on Bayesian statistics are introduced and discussed. In the Bayesian framework, the marginalised model likelihood, or evidence, is the primary model selection statistic.
Mukherjee, Pia, Parkinson, David
openaire   +3 more sources

Assessing the Validity of k-Fold Cross-Validation for Model Selection: Evidence from Bankruptcy Prediction Using Random Forest and XGBoost

Computation
Predicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy.
Vlad Teodorescu, Laura Obreja Brașoveanu   +1 more
doaj   +1 more source

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Geosciences, 2019
Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics,
Jin Li, Justy Siwabessy, Zhi Huang, Scott Nichol   +3 more
doaj   +1 more source

An Introduction to Model Selection

Journal of Mathematical Psychology, 2000
This paper is an introduction to model selection intended for nonspecialists who have knowledge of the statistical concepts covered in a typical first (occasionally second) statistics course. The intention is to explain the ideas that generate frequentist methodology for model selection, for example the Akaike information criterion, bootstrap criteria,
openaire   +3 more sources

Modeling perspective for the relevant market of voice services: Mobile to Mobile

Maskana, 2015
The markets, associated to -mobile to mobile- services of voice, in Advanced Mobile Telecommunications services in Latin America have been subject to dominant operator regulatory processes, which are necessary to attain the market conditions for optimal ...
O. Lucía Quintero, Fabián L. Jaramillo P, Manuela Bastidas   +2 more
doaj   +1 more source