Ros frederic (58 résultats)

Etat : Very Good. : Este libro analiza las relaciones económicas y fiscales entre Cataluña y España. Profesores y políticos catalanes examinan la historia y la articulación política entre Cataluña y España, la balanza fiscal y de capitales, los flujos fiscales entre Cataluña y Europa, y las consecuencias de la dependencia y la viabilidad económica de la independencia de Cataluña. El libro intenta responder a la pregunta de qué se hace con el dinero de los catalanes. EAN: 9788482564982 Tipo: Libros Categoría: Negocios y Economía Título: Catalunya i Espanya: Una relació econòmica i fiscal a revisar Autor: Francesc Cabana| Antoni Castells| Josep Huguet i Biosca| Francesc Ferrer i Gironès| Guillem López i Casasnovas| Andreu Mas-Colell| Esther Martínez i García| Josep Miró i Ardèvol| Marti Parellada| Ferran Pont i Puntigam| Ferran Requejo| Frederic Ribas| Jacint Ros i Hombravella Editorial: Proa Idioma: ca Páginas: 342 Formato: tapa dura.…

Encuadernación de tapa blanda. Etat : Muy bueno. 1ª Edición. Primera edició. Com nou per dins, molt bo per fora, nou no està però poc li falta.

Paperback. Etat : Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock.

Taschenbuch. Etat : Neu. A new density-based sampling for clustering algorithm | Frederic Ros (u. a.) | Taschenbuch | 52 S. | Englisch | 2016 | Scholars' Press | EAN 9783659841354 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Etat : new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : As New. Unread book in perfect condition.

Paperback. Etat : Brand New. 9.26x6.11 inches. In Stock.

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Etat : New.

Etat : New.

Etat : New. In English.

Etat : New.

Etat : New. In English.

Etat : As New. Unread book in perfect condition.

Etat : New.

Gebunden. Etat : New.

Etat : As New. Unread book in perfect condition.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the 'curse of dimensionality', their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task.Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks;Describe implementationand evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality;Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. 'This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge.'M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas'In science the difficulty is not to have ideas, but it is to make them work'From Carlo Rovelli.…

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the 'curse of dimensionality', their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task.Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks;Describe implementationand evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality;Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. 'This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge.'M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas'In science the difficulty is not to have ideas, but it is to make them work'From Carlo Rovelli.…

Etat : New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Taschenbuch. Etat : Neu. Sampling Techniques for Supervised or Unsupervised Tasks | Frédéric Ros (u. a.) | Taschenbuch | Unsupervised and Semi-Supervised Learning | xiii | Englisch | 2020 | Springer | EAN 9783030293512 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New. pp. XIII, 232 40 illus., 30 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Buch. Etat : 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.…

Taschenbuch. Etat : Neu. Feature and Dimensionality Reduction for Clustering with Deep Learning | Frederic Ros (u. a.) | Taschenbuch | Unsupervised and Semi-Supervised Learning | xi | Englisch | 2025 | Springer | EAN 9783031487453 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New.

Etat : New. 1st ed. 2024 edition NO-PA16APR2015-KAP.

Hardcover. Etat : Brand New. 248 pages. 9.25x6.10x0.75 inches. In Stock.

Hardcover. Etat : Brand New. 279 pages. 9.25x6.10x0.91 inches. In Stock.

Paperback. Etat : new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…