Ergebnisse für: AutoML
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Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algori...
Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algori...
Linear Genetic Programming (LGP) represents a significant branch of evolutionary computation that continues to advance both in theoretical understanding and practical applications. This comprehensive volume provides researchers, practitioners, and students with an up-to-date examination of LGP, cove...
Automated Machine Learning for Data-centric Systems provides a system-oriented and knowledge-driven perspective on automated machine learning in modern data-centric environments. As machine learning models become core components of data management systems, the manual design and optimization of model...
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients ar...
This is the first textbook focusing on practicality of machine learning (ML) and deep neural networks (DNN), by introducing methods that enable engineering applications of ML and DNN models. The authors describe many methodologies that are widely used in designing, training, and deploying of these m...
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients ar...
The four-volume set LNAI 15412-15415 constitutes the refereed proceedings of the 34th Brazilian Conference on Intelligent Systems, BRACIS 2024, held in Belém do Pará, Brazil, during November 17–21, 2024.
The 116 full papers presented here were carefully reviewed and selected from 285 submission...
The four-volume set LNAI 16179-16182 constitutes the refereed proceedings of the 35th Brazilian Conference on Intelligent Systems, BRACIS 2025, held in Fortaleza-CE, Brazil, during September 29 - October 2, 2025.
The 147 full papers included in these volumes were carefully reviewed and selected...
The four-volume set LNAI 16179-16182 constitutes the refereed proceedings of the 35th Brazilian Conference on Intelligent Systems, BRACIS 2025, held in Fortaleza-CE, Brazil, during September 29 - October 2, 2025.
The 147 full papers included in these volumes were carefully reviewed and selected...
This book shows Oracle Application Express (APEX) developers how to take advantage of Oracle Cloud Infrastructure (OCI) features for APEX that might otherwise go missed. You will learn how to use OCI features for data science tasks such as detecting anomalies in your data, training machine learning ...
This book shows Oracle Application Express (APEX) developers how to take advantage of Oracle Cloud Infrastructure (OCI) features for APEX that might otherwise go missed. You will learn how to use OCI features for data science tasks such as detecting anomalies in your data, training machine learning ...