This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by "family" to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
"Sinopsis" puede pertenecer a otra edición de este libro.
Frederic Ros has an engineering degree in Microelectronics and Automation, a master’s in Robotics from Montpellier University, and a Ph.D. from ENGREF (Ecole Nationale du Genie Rural des Eaux et Forets, now AgroParistech), Paris. He began his career in 1991 as a research scientist working on the field of image analysis for robotics and artificial systems from IRSTEA (now INRAE). He managed the vision activity in GEMALTO (now THALES) for 14 years which is the world leader in the smart card industry. He was particularly involved in applied developments (related to machine vision, AI, data analysis, fuzzy logic, and supervised classification algorithms) with the aim of providing adaptive and self-tuning systems corresponding to the growing complexity of industrial processes and especially multidisciplinary interactions. Professor at Orleans University, he has been an associate researcher at PRISME laboratory (signal and image processing) for 15 years, focusing his research on machine learning algorithms in the big data context. In parallel, he has headed an innovation park for 12 years and recently designed and structured a digital start-up incubator.
Rabia Riad received the Habilitation degree (HDR) in 2022, from the Ibn Zohr University - Morocco, and the Ph.D. degree in Computer Science from the University of Orleans - France, in 2015. He was a Postdoctoral Fellow at the University of Orleans between 2016 and 2018. Since 2018, he has been with Ibn Zohr University as an Assistant Professor, where he is currently an Associate Professor, since 2022. His research interests include computer vision, artificial intelligence, data analysis, and supervised and unsupervised classification algorithms.
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 17,80 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoEUR 19,49 gastos de envío desde Alemania a España
Destinos, gastos y plazos de envíoLibrería: moluna, Greven, Alemania
Gebunden. Condición: New. Nº de ref. del artículo: 1171875132
Cantidad disponible: Más de 20 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In. Nº de ref. del artículo: ria9783031487422_new
Cantidad disponible: Más de 20 disponibles
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by 'family' to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. 268 pp. Englisch. Nº de ref. del artículo: 9783031487422
Cantidad disponible: 2 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by 'family' to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers. Nº de ref. del artículo: 9783031487422
Cantidad disponible: 1 disponibles
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Condición: New. Nº de ref. del artículo: 47251888-n
Cantidad disponible: Más de 20 disponibles
Librería: California Books, Miami, FL, Estados Unidos de America
Condición: New. Nº de ref. del artículo: I-9783031487422
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 47251888-n
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 47251888
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 47251888
Cantidad disponible: Más de 20 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Buch. Condición: Neu. Neuware -This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and 'tricks' participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by ¿family¿ to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 280 pp. Englisch. Nº de ref. del artículo: 9783031487422
Cantidad disponible: 2 disponibles