Verwandte Artikel zu Logic for Learning: Learning Comprehensible Theories...
Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies) - Softcover
Inhaltsangabe
This book provides an approach to knowledge representation, computation, and learning using higher-order logic. It is aimed at researchers, graduate students, and senior undergraduates working in computational logic and/or machine learning.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 111,52
EUR 17,28 für den Versand von Vereinigtes Königreich nach USA
Versandziele, Kosten & DauerNeu kaufen
Diesen Artikel anzeigen
EUR 53,69
EUR 2,26 für den Versand innerhalb von/der USA
Versandziele, Kosten & Dauer
Suchergebnisse für Logic for Learning: Learning Comprehensible Theories...
Foto des Verkäufers
Logic for Learning : Learning Comprehensible Theories from Structured Data
Anbieter: GreatBookPrices, Columbia, MD, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 11874534-n
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Logic for Learning (Paperback)
ISBN 10: 3642075533
ISBN 13: 9783642075537
Neu
Paperback
Erstausgabe
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning. This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783642075537
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
Anbieter: Lucky's Textbooks, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers ABLIING23Mar3113020216388
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9783642075537_new
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Logic for Learning : Learning Comprehensible Theories from Structured Data
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 11874534-n
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Logic for Learning
Verlag:
Springer Berlin Heidelberg Okt 2010, 2010
ISBN 10: 3642075533
ISBN 13: 9783642075537
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it provides a framework for knowledge representation and computation based on higher-order logic, and demonstrates its advantages over more standard approaches based on first-order logic. For those interested in machine learning, the book explains how higher-order logic provides suitable knowledge representation formalisms and hypothesis languages for machine learning applications. This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed, and for those in machine learning no previous knowledge of computational logic is assumed.The logic used throughout the book is a higher-order one, since higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation.The book should be of interest to researchers in machine learning, especially those who study learning methods for structured data. Throughout, great emphasis is placed on learning comprehensible theories. The book serves as an introduction for computational logicians to machine learning, a particularly interesting and important application area of logic, and also provides a foundation for functional logic programming languages. 272 pp. Englisch. Bestandsnummer des Verkäufers 9783642075537
Anzahl: 2 verfügbar
Foto des Verkäufers
Logic for Learning
Print-on-DemandAnbieter: moluna, Greven, Deutschland
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book on learning and knowledge representation based on higher-order logic.This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it. Bestandsnummer des Verkäufers 5046619
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Logic for Learning
ISBN 10: 3642075533
ISBN 13: 9783642075537
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch. Bestandsnummer des Verkäufers 9783642075537
Anzahl: 1 verfügbar
Foto des Verkäufers
Logic for Learning : Learning Comprehensible Theories from Structured Data
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning. Bestandsnummer des Verkäufers 9783642075537
Anzahl: 1 verfügbar
Foto des Verkäufers
Logic for Learning : Learning Comprehensible Theories from Structured Data
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 11874534
Anzahl: Mehr als 20 verfügbar