Rezultaty - (( basic difference relocation algorithms ) OR ( basic difference relation algorithm ))*

Metrology

Dokumenty pełnotekstowe

X-Machines for Agent-Based Modeling od Kiran, Mariam

Dokumenty pełnotekstowe

Tensor Network Contractions od Ran, Shi-Ju, Tirrito, Emanuele, Peng, Cheng, Chen, Xi, Tagliacozzo, Luca, Su, Gang, Lewenstein, Maciej

“…To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. …”
Dokumenty pełnotekstowe

Advances in Near Infrared Spectroscopy and Related Computational Methods od Bec, Krzysztof B., Huck, Christian

Dokumenty pełnotekstowe

Entropy Measures for Data Analysis: Theory, Algorithms and Applications od Keller, Karsten

Dokumenty pełnotekstowe

The Foundation of Precision Medicine: Integration of Electronic Health Records with Nenomics Through Basic, Clinical, and Translational Research od Mariza de Andrade, Helena Kuivaniemi, Marylyn D. Ritchie

“…Chapter 2 describes the results of genetic studies on different diseases for which all the phenotypic information was extracted from the EHR with highly specific ePhenotyping algorithms. …”
Dokumenty pełnotekstowe

Individual differences in associative learning od Rachel M. Msetfi, Robin A. Murphy

“…This work involves varying stimulus properties and temporal relations or modeling the differences between groups.…”
Dokumenty pełnotekstowe

Discrete Event Simulations

“…The range of application of DES spans across many different disciplines and research fields. In research, further development and advancements of the basic DES algorithm continue to be sought while various hybrid methods derived by combining DES with other simulation techniques continue to be developed. …”
Dokumenty pełnotekstowe

Uncertain Multi-Criteria Optimization Problems

Dokumenty pełnotekstowe

Metalearning od Brazdil, Pavel, van Rijn, Jan N., Soares, Carlos, Vanschoren, Joaquin

“…This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. …”
Dokumenty pełnotekstowe