Ma haibo (28 resultados)

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: new.

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Condición: As New. Unread book in perfect condition.

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Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. pp. 224.

Condición: New. In English.

Condición: New. pp. 224.

Condición: New. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. Editor(s): He, Haibo; Ma, Yunqian. Num Pages: 216 pages, illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 236 x 162 x 19. Weight in Grams: 494. . 2013. 1st Edition. Hardcover. . . . . …

Hardcover. Condición: Brand New. 1st edition. 216 pages. 9.40x6.20x0.80 inches. In Stock.

Condición: New. This book certainly qualifies as a reference for graduate studies in machine learning. Research students are sure to find it highly valuable and a prized possession, especially taking into account the wealth of supporting literature that the authors have b.

Condición: New. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. Editor(s): He, Haibo; Ma, Yunqian. Num Pages: 216 pages, illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 236 x 162 x 19. Weight in Grams: 494. . 2013. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

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Buch. Condición: Neu. Neuware - The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learningImbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation.The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on:\* Foundations of Imbalanced Learning\* Imbalanced Datasets: From Sampling to Classifiers\* Ensemble Methods for Class Imbalance Learning\* Class Imbalance Learning Methods for Support Vector Machines\* Class Imbalance and Active Learning\* Nonstationary Stream Data Learning with Imbalanced Class Distribution\* Assessment Metrics for Imbalanced LearningImbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.…

Condición: New.

Condición: New.

Taschenbuch. Condición: Neu. Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry | Haibo Ma (u. a.) | Taschenbuch | Englisch | 2022 | Elsevier Inc | EAN 9780323856942 | Verantwortliche Person für die EU: Kolibri 360 GmbH, Ettore-Bugatti-Str. 6-14, 51149 Köln, produktsicherheit[at]kolibri360[dot]de | Anbieter: preigu. …

Condición: New. In English.

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Condición: New. Provides an expertly-curated, consolidated overview of research in the field Includes exercises that support learning and link theory to practice Outlines key theories and algorithms for computational chemistry applications.

Hardcover. Condición: Brand New. 1st edition. 216 pages. 9.40x6.20x0.80 inches. In Stock. This item is printed on demand.

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 524.

Hardcover. Condición: new. Hardcover. The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced LearningImbalanced Datasets: From Sampling to ClassifiersEnsemble Methods for Class Imbalance LearningClass Imbalance Learning Methods for Support Vector MachinesClass Imbalance and Active LearningNonstationary Stream Data Learning with Imbalanced Class DistributionAssessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry outlines important theories and algorithms of DMRG-based approaches and explores their use in computational chemistry. Beginning with an introduction to DMRG and DMRG-based approaches, the book goes on to discuss the key theories and applications of DMRG, from DMRG for semi-empirical and ab-initio quantum chemistry, to DMRG in embedded environments, frequency spaces and quantum dynamics. Drawing on the experience of its expert authors, sections detail recent ideas and key developments, providing an up-to-date view of current developments in the field for students and researchers in quantum chemistry. 336 pp. Englisch.…

Condición: New. PRINT ON DEMAND pp. 224.