ISBN: 9786209379475 - research advances in machine learning (10 risultati)

Condizione: New.

Condizione: New.

Taschenbuch. Condizione: Neu. Research Advances in Machine Learning | Youness Manzali (u. a.) | Taschenbuch | Englisch | 2025 | KS Omniscriptum Publishing | EAN 9786209379475 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This advanced research book introduces well-designed improvements to major machine learning algorithms, including decision trees, KNN, random forests, and clustering methods. The book focuses on reducing model complexity while improving accuracy, robustness, and execution time through novel pruning, feature weighting, and ensemble strategies. The proposed approaches are experimentally validated on well-known real-world datasets, demonstrating their effectiveness and strong practical relevance. 160 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This advanced research book introduces well-designed improvements to major machine learning algorithms, including decision trees, KNN, random forests, and clustering methods. The book focuses on reducing model complexity while improving accuracy, robustness, and execution time through novel pruning, feature weighting, and ensemble strategies. The proposed approaches are experimentally validated on well-known real-world datasets, demonstrating their effectiveness and strong practical relevance.…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. This advanced research book introduces well-designed improvements to major machine learning algorithms, including decision trees, KNN, random forests, and clustering methods. The book focuses on reducing model complexity while improving accuracy, robustness, and execution time through novel pruning, feature weighting, and ensemble strategies. The proposed approaches are experimentally validated on well-known real-world datasets, demonstrating their effectiveness and strong practical relevance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Condizione: New. PRINT ON DEMAND.