A Manuel for the Classification, Training and Education of the Feeble-minded, Imbecile, & Idiotic PDF Download
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Author: Peter Martin Duncan
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
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Book Description
Author: Peter Martin Duncan
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
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Book Description
Author: Olivier Bousquet
Publisher: Springer
ISBN: 3540286500
Category : Computers
Languages : en
Pages : 249
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Book Description
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
Author:
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ISBN:
Category : Communicable diseases
Languages : en
Pages : 374
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Book Description
Author: Taeho Jo
Publisher: Springer Nature
ISBN: 3030659003
Category : Technology & Engineering
Languages : en
Pages : 391
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Book Description
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 928
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Book Description
Author: YoonJoon Lee
Publisher: Springer Science & Business Media
ISBN: 3540210474
Category : Computers
Languages : en
Pages : 948
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Database Systems for Advanced Applications, DASFAA 2004, held in Jeju Island, Korea in March 2004. The 60 revised full papers and 18 revised short papers presented together with 2 invited articles were carefully reviewed and seleted from 272 submissions. The papers are organized in topical sections on access methods, query processing in XML, security and integrity, query processing in temporal and spatial databases, semi-structured databases, knowledge discovery in temporal and spatial databases, XML and multimedia and knowledge discovery on the Web, query processing and optimization, classification and clustering, Web search, mobile databases, parallel and distributed databases, and multimedia databases.
Author:
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 978
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Book Description
Author:
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 722
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Book Description
Author: Masashi Sugiyama
Publisher: MIT Press
ISBN: 0262047071
Category : Mathematics
Languages : en
Pages : 315
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Book Description
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom. The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation.
