Vatsa Mayank (67 results)

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: New.

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

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

Condition: As New. Unread book in perfect condition.

Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Condition: New.

Condition: New.

Condition: New. In English.

Condition: New.

Paperback. Condition: Brand New. 328 pages. 9.19x6.13x0.79 inches. In Stock.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New. Mayank Vatsa is an Associate Professor at IIIT New Delhi. He has authored more than 150 publications dealing with biometrics, image processing, machine learning and information fusion. He is a Senior Member of IEEE.

Condition: New.

Condition: New.

Condition: New. In English.

Condition: New. In English.

Condition: New.

Condition: New. In English.

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: As New. Unread book in perfect condition.

Taschenbuch. Condition: Neu. Neuware - Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start-ups are focusing on deep learning-based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book covers topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoencoders. The focus is also on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints, while examining the future trends in deep learning and biometric research. - Contains chapters written by authors who are leading researchers in biometrics. - Presents a comprehensive overview on the internal mechanisms of deep learning. - Discusses the latest developments in biometric research. - Examines future trends in deep learning and biometric research. - Provides extensive references at the end of each chapter to enhance further study.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition.Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presentsa technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods.This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition.Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presentsa technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods.This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding. …