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Semi-supervise d Learning - Hardcover, by Chapelle Olivier; Scholkopf - Very Good
US $14.68
ApproximatelyS$ 18.92
Condition:
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Located in: Philadelphia, Pennsylvania, United States
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Estimated between Wed, 8 Oct and Wed, 15 Oct to 94104
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eBay item number:406055342616
Item specifics
- Condition
- Book Title
- Semi-supervised Learning (Adaptive Computation And Machine Learni
- ISBN
- 9780262033589
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262033585
ISBN-13
9780262033589
eBay Product ID (ePID)
52758325
Product Key Features
Number of Pages
498 Pages
Language
English
Publication Name
Semi-Supervised Learning
Publication Year
2006
Subject
Intelligence (Ai) & Semantics
Type
Textbook
Subject Area
Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover
Dimensions
Item Height
1.2 in
Item Weight
45.1 Oz
Item Length
10.2 in
Item Width
8.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2006-044448
Reviews
"In summary, reading this book is a delightful journey through semi-supervisedlearning." Hsun-Hsien Chang Computing Reviews, "In summary, reading this book is a delightful journey through semi-supervised learning." Hsun-Hsien Chang Computing Reviews
Dewey Edition
22
Grade From
College Graduate Student
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction., A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research.
LC Classification Number
Q325.75.S42 2006
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