Articles liés à Data Mining and Computational Intelligence
Synopsis
Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 157,81
EUR 3,42 expédition vers Etats-Unis
Destinations, frais et délais
Résultats de recherche pour Data Mining and Computational Intelligence
Image d'archives
Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur ABLIING23Apr0316110061538
Quantité disponible : Plus de 20 disponibles
Image d'archives
Data Mining and Computational Intelligence (Studies in Fuzziness and Soft Computing)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9783790824841_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD Okt 2010, 2010
ISBN 10 : 3790824844
ISBN 13 : 9783790824841
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., 'most students used to be profitable') and the patterns of the future (e.g., 'students will be profitable'). 372 pp. Englisch. N° de réf. du vendeur 9783790824841
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD, 2010
ISBN 10 : 3790824844
ISBN 13 : 9783790824841
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive coverage of recent advances in the application of soft computing and fuzzy logic data miningAlso useful as a reference book in data mining, machine learning, fuzzy logic, and artificial intelligenceMany business decisions are made in t. N° de réf. du vendeur 5310750
Quantité disponible : Plus de 20 disponibles
Image d'archives
Data Mining and Computational Intelligence
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. pp. 372. N° de réf. du vendeur 262157752
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
Data Mining and Computational Intelligence
Edité par
Physica-Verlag HD Okt 2010, 2010
ISBN 10 : 3790824844
ISBN 13 : 9783790824841
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., 'most students used to be profitable') and the patterns of the future (e.g., 'students will be profitable').Physica Verlag, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch. N° de réf. du vendeur 9783790824841
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Data Mining and Computational Intelligence
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., 'most students used to be profitable') and the patterns of the future (e.g., 'students will be profitable'). N° de réf. du vendeur 9783790824841
Quantité disponible : 1 disponible(s)
Image d'archives
Data Mining and Computational Intelligence
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Print on Demand pp. 372 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. N° de réf. du vendeur 5722983
Quantité disponible : 4 disponible(s)
Image d'archives
Data Mining and Computational Intelligence
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. PRINT ON DEMAND pp. 372. N° de réf. du vendeur 182157746
Quantité disponible : 4 disponible(s)