drugowitsch (37 resultados)

Paperback. Condición: new. Paperback. This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, andin Montreal, Canada, in July 2009 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO.The 12 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on LCS in general, function approximation, LCS in complex domains, and applications. This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, andin Montreal, Canada, in July 2009 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Paperback. Condición: Brand New. 209 pages. 9.50x6.25x0.50 inches. In Stock.

Condición: New. Up-to-date results in learning classifier systemsThis book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, .

Condición: New. In.

Taschenbuch. Condición: Neu. Neuware - This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, andin Montreal, Canada, in July 2009 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO.The 12 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on LCS in general, function approximation, LCS in complex domains, and applications.

Condición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condición: New. pp. 284.

Paperback. Condición: Brand New. 268 pages. 9.00x6.00x0.64 inches. In Stock.

Taschenbuch. Condición: Neu. Design and Analysis of Learning Classifier Systems | A Probabilistic Approach | Jan Drugowitsch | Taschenbuch | Studies in Computational Intelligence | xiv | Englisch | 2010 | Springer | EAN 9783642098611 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition - derived from machine learning - of 'a good set of cl- si ers', based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of 'good set of classi ers' (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.

Condición: New. In.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition - derived from machine learning - of 'a good set of cl- si ers', based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of 'good set of classi ers' (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.

Paperback. Condición: Like New. Like New. book.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Meine norwegischen Großeltern, Eivind Høeg und Ingrid Høeg, geb. Hjertaas, haben in Bergen, Fjellveien 45 gewohnt. Meine Mutter ist dort aufgewachsen. Das Buch 'Ich würde gerne Hjertaas heißen' handelt von den Memoiren meiner Mutter und der Abstammung der Großmutter Ingrid. Im vorliegenden Buch geht es neben dem Leben in Bergen im 2.Weltkrieg vor allem um die Familie meines Großvaters Eivind bis zurück ins 14. Jahrhundert nach Dänemark. Im Gegensatz zu den Hjertaas, die von einer Insel im Sognefjord stammen und nach der Übersiedlung in Bergen als Kaufleute begannen, waren die Høeg eine gutbürgerliche Familie mit adeligen Vorfahren. Zusammen mit meinem Buch 'Die ewige Jugend des Otto D.' über meinen Vater habe ich versucht, ausgehend vom Leben meiner Eltern einen umfassenden Überblick über deren Vorfahren im geschichtlichen Kontext zu geben. 80 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 80 pp. Deutsch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Peter Drugowitsch wurde am 29.10.1944 im 2. Weltkrieg während eines Luftangriffs der Royal Air Force auf den deutschen U-Boot Stützpunkt in Laksevåg unter dramatischen Umständen in einem deutschen Militärspital in Bergen an der Westküste Norwegens geboren.Seine Mutter war eine junge Norwegerin, sein Vater ein österreichischer Bauingenieur im Dienst der Deutschen Wehrmacht.In diesem Umfeld spielt diese faszinierende Geschichte in den Wirren der Zeit - gezeichnet vom Krieg, Liebe und starkem Willen. Die Memoiren der Mutter, Elsa Lillian Drugowitsch geb. Høeg, von ihrer Jugend bis zum Kriegsende und die Übersiedlung nach Österreich im Jahr 1946 sind ein einmaliges Dokument.Die Vorfahren mütterlicherseits stammen von einem einsamen Hof auf der Insel Hisarøy, im Sognefjord an der Nordsee gelegen. Die Suche nach seinen Wurzeln führte den Autor an diesen Ort und zurück in die Vergangenheit. 80 pp. Deutsch.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware 80 pp. Englisch.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Meine norwegischen Großeltern, Eivind Høeg und Ingrid Høeg, geb. Hjertaas, haben in Bergen, Fjellveien 45 gewohnt. Meine Mutter ist dort aufgewachsen. Das Buch 'Ich würde gerne Hjertaas heißen' handelt von den Memoiren meiner Mutter und der Abstammung der Großmutter Ingrid. Im vorliegenden Buch geht es neben dem Leben in Bergen im 2.Weltkrieg vor allem um die Familie meines Großvaters Eivind bis zurück ins 14. Jahrhundert nach Dänemark. Im Gegensatz zu den Hjertaas, die von einer Insel im Sognefjord stammen und nach der Übersiedlung in Bergen als Kaufleute begannen, waren die Høeg eine gutbürgerliche Familie mit adeligen Vorfahren. Zusammen mit meinem Buch 'Die ewige Jugend des Otto D.' über meinen Vater habe ich versucht, ausgehend vom Leben meiner Eltern einen umfassenden Überblick über deren Vorfahren im geschichtlichen Kontext zu geben.

Buch. Condición: Neu. Eivind Emil Høeg Familienspuren. Life is a Story - [.] | Peter Drugowitsch | Buch | Englisch | 2026 | [.] publishing | EAN 9783711810663 | Verantwortliche Person für die EU: Storylution GmbH, Biberstr. 5, 1010 WIEN, ÖSTERREICH, story[dot]one[at]story[dot]one | Anbieter: preigu Print on Demand.

Condición: new. Questo è un articolo print on demand.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition - derived from machine learning - of 'a good set of cl- si ers', based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of 'good set of classi ers' (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS. 284 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition - derived from machine learning - of 'a good set of cl- si ers', based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of 'good set of classi ers' (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS. 284 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research in the area of Learning Classifier SystemsPresents a probabilistic approach to Design and Analysis of Learning Classifier SystemsThis book is probably best summarized as providing a principled foundation for Learning Classi.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research in the area of Learning Classifier SystemsPresents a probabilistic approach to Design and Analysis of Learning Classifier SystemsThis book is probably best summarized as providing a principled foundation for Learning Classi.

Condición: New. Print on Demand pp. 284 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 284.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition ¿ derived from machine learning ¿ of ¿a good set of cl- si ers¿, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of ¿good set of classi ers¿ (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is probably best summarized as providing a principled foundation for Learning Classi er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de nition ¿ derived from machine learning ¿ of ¿a good set of cl- si ers¿, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi ers using that de nition as a tness criterion, seeing ifthe setprovidesa goodsolutionto twodi erent function approximation problems. It appears to, meaning that in some sense his de nition of ¿good set of classi ers¿ (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch.