Data Representation Preprocessing Dimensionality by Kumari Parveen (6 results)

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 244 pp. Englisch.

Taschenbuch. Condition: Neu. Data Representation, Preprocessing, and Dimensionality Reduction | Parveen Kumari (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630237849 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data Representation, Preprocessing, and Dimensionality Reduction provides a comprehensive introduction to the essential techniques used for preparing data for analytics, machine learning, and artificial intelligence applications. The book explores methods for representing structured and unstructured data, handling missing values, noise, and inconsistencies, and transforming raw data into meaningful formats. It also covers feature selection, feature extraction, normalization, encoding, and dimensionality reduction techniques such as PCA and other modern approaches. Through practical examples and real-world applications, the book demonstrates how effective data preprocessing and dimensionality reduction improve model performance, computational efficiency, and decision-making accuracy in data-driven environments.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 244 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data Representation, Preprocessing, and Dimensionality Reduction provides a comprehensive introduction to the essential techniques used for preparing data for analytics, machine learning, and artificial intelligence applications. The book explores methods for representing structured and unstructured data, handling missing values, noise, and inconsistencies, and transforming raw data into meaningful formats. It also covers feature selection, feature extraction, normalization, encoding, and dimensionality reduction techniques such as PCA and other modern approaches. Through practical examples and real-world applications, the book demonstrates how effective data preprocessing and dimensionality reduction improve model performance, computational efficiency, and decision-making accuracy in data-driven environments.…