computational models learning (135 résultats)

Paperback. Etat : Very Good. Very Good paperback with light shelfwear - NICE! Standard-sized.

hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Wide 8vo, 411 pp. Wrapper corners lightly bumped, previous owner name.

Etat : Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

Softcover. Etat : Very Good+. First Edition. Light rubbing, small corner creases, spine slightly faded. ; Psychology Of Learning And Motivation, Vol 23; 8vo 8" - 9" tall.

Etat : Good. Sticker on cover. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some wear and internal barcode may have been clipped, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

gebundene Ausgabe. Etat : Gut. 466 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten Bibliothek und kann die entsprechenden Kennzeichnungen aufweisen (Rückenschild, Instituts-Stempel.); der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 925.

Hardcover. Etat : Fine. B00K: Fine/, 1987 (illustrateur). B00K: Fine/, $187.89, Reduced from. 0387163182 Computational Models of Learning; Springer Series Symbolic Computation - Artificial Intelligence. With 34 Figures. * Bolc, Leonard; Bradshaw; Langley; Michalski; Ohlsson; Rendell; Simon. Springer-Verlag, Berlin and Heidelberg, NY, Tokyo 1987 UnStated, 1sT Edition Slivery Spine With Title In Black And Yellow Letters, Hard Cover B00K: Fine/, 208 Numbered Pages, That Were Lightly Read, Are Clean And Tight To The Spine, Slight Shelf, Edge And Book Was Dropped On The Top Rear Corner And Forward Creating A Drop Mark In The Top And On Some Pages. = SYNOPSIS: "In Recent Years, Machine Learning Has Emerged As A Significant Area Of Research In Artificial Intelligence And Cognitive Science. At Present, Research In The Field Is Being Intensified From Both The Point Of View Of Theory And Of Implementation, And The Results Are Being Introduced More.In Practice. Machine Learning Has Recently Become The Subject Of Interest Of Many Young And Talented Scientists Whose Bold Ideas Have Greatly Contributed To The Broadening Of Knowledge In This Rapidly Developing Field Of Science. This Situation Has Manifested Itself In An Increasing Number Of Valuable Contributions To Scientific Journals. However, Such Papers Are Necessarily Compact Descriptions Of Research Problems. Computational Models Of Learning Supplements These Contributions And Is A Collection Of More Extensive Essays. These Essays Provide The Reader With An Increased Knowledge Of Carefully Selected Problems Of Machine Learning". Description Applies To This B0K, Only, Which Is Hard To Find, And Will Be Packaged And Shipped = Carefully, To Avoid Shipping Damage And Will Make It, An Excellent Addition To Your Own Personal Library Collection, Or As A Gift For The Collector / Reader. = World Wide Shipping = AVAILABLE =.

hardcover. Etat : Very Good. Etat de la jaquette : very good. A nice copy. Clean text, solid binding. Dust Jacket protected by mylar cover.

Etat : Good. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

First edition. 318 pages. Cloth bound in near very good condition; Some wear to spine edges.

Hardcover. Etat : Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 1997. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Edition 1997. Ammareal gives back up to 15% of this item's net price to charity organizations.

Etat : Gut. Auflage: New ed. 416 Seiten ex Library Book / aus einer wissenschafltichen Bibliothek / Sprache: Englisch Gewicht in Gramm: 969 22,1 x 14,7 x 2,5 cm, Taschenbuch.

Etat : Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. A few small stains.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. In.

hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Hardcover. Etat : New.

Etat : New.

Etat : New.

Etat : New.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Although there is much literature on organisational learning, mathematical formalisation and computational simulation, there is no literature that uses mathematical modelling and simulation to represent and explore different facets of multilevel learning. This book provides an overview of recent work on mathematical formalisation and computational simulation of multilevel organisational learning by exploiting the possibilities of self-modeling network models to address it. This is the first book addressing mathematical formalisation and computational modeling of multilevel organisational learning in a systematic, principled manner. A self-modeling network modeling approach from AI and Network Science is used where in a reflective manner some of the network nodes (called self-model nodes) represent parts of the network's own network structure characteristics. This is supported by a dedicated software environment allowing to design and implement (higher-order) adaptive network models by specifying them in a conceptual manner at a high level of abstraction in a standard table format, without any need of algorithmic specification or programming. This modeling approach allows to model the development of knowledge in an organisational setting in a neatly structured manner at three different levels for the usage, adaptation and control, respectively, of the underlying mental models. Several examples of realistic cases of multilevel organisational learning are used to illustrate the approach. Crucial concepts such as the aggregation of mental models to form shared mental models out of individual mental models are addressed extensively. It is shown how to model context-sensitive control of organisational learning taking into account a wide variety of context factors, for example relating to levels of expertise of individuals or to leadership styles of managers involved. Mathematical equilibrium analysis of models of organisational learning is also addressed, among others allowing verification of correctness of the implemental models in comparison to their conceptual design.Chapters in this book also contribute to the Management and Business Sciences research by demonstrating how computational modeling can be used to capture complex management phenomena such as multilevel organizational learning. This book has a potential implication for practice by demonstrating how computational modeling can be used to capture learning scenarios, which then provide a basis for more informed managerial decisions.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Ensuring patient safety and security through cyberspace requires that all care professionals operate as a team and community . In order to be successful, it is of paramount importance that all members of the team have a shared understanding of the diagnosis, the condition of the patient, the secure use of medical devices and the plan of action. At present, to ensure that all mem bers have this 'shared mental model', members communicate and observe each other's actions. Based upon this information, members sho uld be able to confirm if indeed all have a shared mental model and speak up when deviations in one or more members and/or processes are suspected. From a group dynamical and information processing perspective, this verification process is known to be very vulnerable: how can red flags be detected in complicated surgery settings and do members feel psychologically safe enough to speak up when they have concerns about being on the same page as the rest of their team This book presents a new approach for saf ety and security through cyberspace through introducing a concept of co designed clinical pathways supported by the AI coach. The AI coach will be an intervention for both improving hospital wide safety and security through cyberspace. The AI Coach will empower users by supporting and facilitating the development of a shared mental model for team and organisational learning. The AI coach will function as an information, communication, cooperation and decision support system. The book advises to incorporate issues or cybersecurity risk management into the total safety and security process, among others through co-creating security.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Although there is much literature on organisational learning, mathematical formalisation and computational simulation, there is no literature that uses mathematical modelling and simulation to represent and explore different facets of multilevel learning. This book provides an overview of recent work on mathematical formalisation and computational simulation of multilevel organisational learning by exploiting the possibilities of self-modeling network models to address it. This is the first book addressing mathematical formalisation and computational modeling of multilevel organisational learning in a systematic, principled manner. A self-modeling network modeling approach from AI and Network Science is used where in a reflective manner some of the network nodes (called self-model nodes) represent parts of the network's own network structure characteristics. This is supported by a dedicated software environment allowing to design and implement (higher-order) adaptive network models by specifying them in a conceptual manner at a high level of abstraction in a standard table format, without any need of algorithmic specification or programming. This modeling approach allows to model the development of knowledge in an organisational setting in a neatly structured manner at three different levels for the usage, adaptation and control, respectively, of the underlying mental models. Several examples of realistic cases of multilevel organisational learning are used to illustrate the approach. Crucial concepts such as the aggregation of mental models to form shared mental models out of individual mental models are addressed extensively. It is shown how to model context-sensitive control of organisational learning taking into account a wide variety of context factors, for example relating to levels of expertise of individuals or to leadership styles of managers involved. Mathematical equilibrium analysis of models of organisational learning is also addressed, among others allowing verification of correctness of the implemental models in comparison to their conceptual design.Chapters in this book also contribute to the Management and Business Sciences research by demonstrating how computational modeling can be used to capture complex management phenomena such as multilevel organizational learning. This book has a potential implication for practice by demonstrating how computational modeling can be used to capture learning scenarios, which then provide a basis for more informed managerial decisions.

Etat : New. In.