In the area of artificial learners, not much research on the question of an appropriate description of artificial learner’s (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm’s internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet. This work addresses the problem of measuring and observing the artificial learners, specifically the decision trees produced by the C4.5 algorithm. A procedure for measuring the learning progress, called adaptive incremental k-fold cross-validation is presented, together with other tools and techniques needed to observe artificial learners on their course of learning. Early observations can be used to forecast the future performance of a learner based on a small training sample.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet. This work addresses the problem of measuring and observing the artificial learners, specifically the decision trees produced by the C4.5 algorithm. A procedure for measuring the learning progress, called adaptive incremental k-fold cross-validation is presented, together with other tools and techniques needed to observe artificial learners on their course of learning. Early observations can be used to forecast the future performance of a learner based on a small training sample. 172 pp. Englisch. N° de réf. du vendeur 9783659562518
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Etat : New. pp. 172. N° de réf. du vendeur 26128432546
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand pp. 172 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. N° de réf. du vendeur 131073661
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Etat : New. PRINT ON DEMAND pp. 172. N° de réf. du vendeur 18128432552
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Assessment of Classification Algorithms in Artificial Intelligence | Bo¿tjan Brumen | Taschenbuch | 172 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659562518 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. N° de réf. du vendeur 105184314
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet. This work addresses the problem of measuring and observing the artificial learners, specifically the decision trees produced by the C4.5 algorithm. A procedure for measuring the learning progress, called adaptive incremental k-fold cross-validation is presented, together with other tools and techniques needed to observe artificial learners on their course of learning. Early observations can be used to forecast the future performance of a learner based on a small training sample.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 172 pp. Englisch. N° de réf. du vendeur 9783659562518
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet. This work addresses the problem of measuring and observing the artificial learners, specifically the decision trees produced by the C4.5 algorithm. A procedure for measuring the learning progress, called adaptive incremental k-fold cross-validation is presented, together with other tools and techniques needed to observe artificial learners on their course of learning. Early observations can be used to forecast the future performance of a learner based on a small training sample. N° de réf. du vendeur 9783659562518
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