Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure

2015 ◽  
pp. 327-346
Author(s):  
Kweku-Muata Osei-Bryson
Keyword(s):  
Generation Procedure ◽  
Ensemble Generation

scholarly journals Strategies for hydrologic ensemble generation and calibration: on the merits of using model-based predictors

2021 ◽  
pp. 126233
Author(s):  
Anne-Laure Tiberi-Wadier ◽  
Nicole Goutal ◽  
Sophie Ricci ◽  
Philippe Sergent ◽  
Maxime Taillardat ◽  
...  
Keyword(s):  
Model Based ◽  
Ensemble Generation

scholarly journals The Decadal Climate Prediction Project

2016 ◽  
Author(s):  
George J. Boer ◽  
Douglas M . Smith ◽  
Christophe Cassou ◽  
Francisco Doblas-Reyes ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Climate Prediction ◽  
Decadal Prediction ◽  
Science And Society ◽  
Climate Data ◽  
Climate Shifts ◽  
Component C ◽  
Decadal Climate ◽  
Decadal Climate Prediction ◽  
Ensemble Generation

Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from CMIP5 and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as part of CMIP6. The DCPP consists of three Components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, dissemination and analysis of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the "hiatus", volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the Components of the DCPP, each of which are separately prioritized, as are of interest to them. The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.

scholarly journals Skill assessment of different ensemble generation schemes for retrospective predictions of surface freshwater fluxes on inter and multi-annual timescales

2018 ◽  
Vol 27 (2) ◽  
pp. 111-124 ◽  
Author(s):  
Vanya Romanova ◽  
Andreas Hense ◽  
Sabrina Wahl ◽  
Sebastian Brune ◽  
Johanna Baehr
Keyword(s):  
Skill Assessment ◽  
Freshwater Fluxes ◽  
Ensemble Generation

scholarly journals Learning Personalized Itemset Mapping for Cross-Domain Recommendation

2020 ◽  
Author(s):  
Yinan Zhang ◽  
Yong Liu ◽  
Peng Han ◽  
Chunyan Miao ◽  
Lizhen Cui ◽  
...  
Keyword(s):  
Single Domain ◽  
Model Parameters ◽  
The Real ◽  
Cross Domain ◽  
Proposed Model ◽  
Latent Space ◽  
Learning Parameter ◽  
Generation Procedure ◽  
Over Time

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

scholarly journals A Distributed Shared Key Generation Procedure Using Fractional Keys

1998 ◽  
Author(s):  
R. Poovendran ◽  
M. S. Corson ◽  
J. S. Baras
Keyword(s):  
Key Generation ◽  
Shared Key ◽  
Generation Procedure
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