Yunqian ma (33 Ergebnisse)

Zustand: Fine. 2017 printing; 340 pp., hardcover, previous owner's name neatly inked to the title page, head of spine rubbed, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.…

Zustand: new.

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

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New. In English.

Zustand: New. pp. 224.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.…

Zustand: 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. . . . . …

Paperback. Zustand: Brand New. 268 pages. 9.30x6.20x0.63 inches. In Stock.

Taschenbuch. Zustand: Neu. Graph Embedding for Pattern Analysis | Yun Fu (u. a.) | Taschenbuch | viii | Englisch | 2014 | Springer | EAN 9781489990624 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Hardcover. Zustand: Brand New. 2013 edition. 268 pages. 9.20x6.10x0.80 inches. In Stock.

Gebunden. Zustand: 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.

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

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 312 | Sprache: Englisch | Produktart: Bücher | Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.…

Hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Zustand: 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. …

Zustand: New.

Buch. Zustand: 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. …

Taschenbuch. Zustand: Neu. Support Vector Machines Applications | Yunqian Ma (u. a.) | Taschenbuch | vii | Englisch | 2016 | Springer | EAN 9783319343297 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications. …

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.…

Zustand: New.

Zustand: New.

Taschenbuch. Zustand: Neu. Ensemble Machine Learning | Methods and Applications | Yunqian Ma (u. a.) | Taschenbuch | viii | Englisch | 2014 | Springer US | EAN 9781489988171 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Hardcover. Zustand: Brand New. 2014 edition. 298 pages. 9.25x6.25x1.00 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed 'ensemble learning' by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as 'boosting' and 'random forest' facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.…

Hardcover. Zustand: Brand New. 1st edition. 376 pages. 10.16x6.93x1.02 inches. In Stock.