machine learning hybridization optimization (15 resultados)

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New. 1st edition NO-PA16APR2015-KAP.

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Condición: New.

Condición: New.

Hardcover. Condición: Brand New. 384 pages. 9.18x6.12x9.21 inches. In Stock.

Hardcover. Condición: new. Hardcover. This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tanvir Habib Sardar is an Assistant Professor in the department of CSE at GITAM University, Bengaluru campus. He has more than fifteen years of experience in industry and academia. His research domain is big data, machine learning, fuzzy logic, and distr.

Hardcover. Condición: new. Hardcover. This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. Machine Learning Hybridization and Optimization for Intelligent Applications | Tanvir Habib Sardar (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | CRC Press | EAN 9781032737539 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.