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A model of selective migration [PDF]

Heredity, 1974
Individuals migrating between populations are normally assumed to be drawn at random from their base populations; migration is then a strong unifying force between the populations. Phenotypic assortment of migrants could however cause populations to diverge. A model is formulated to describe the effects of such selective migration on a metric character,
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Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

Frontiers in Veterinary Science, 2017
Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial ...
Anke Hüls, Anke Hüls, Cornelia Frömke, Katja Ickstadt, Katja Hille, Johanna Hering, Christiane von Münchhausen, Maria Hartmann, Lothar Kreienbrock   +8 more
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Models for chronology selection [PDF]

Physical Review D, 1998
20 pages ...
Cassidy, M. J., Hawking, S. W.
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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

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
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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
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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
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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
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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
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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,
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