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Semi-supervised Learning - Hardcover, by Chapelle Olivier; Scholkopf - Very Good

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Specifiche dell'oggetto

Condizione
Ottime condizioni: Libro che non sembra nuovo ed è già stato letto, ma è in condizioni eccellenti. ...
Book Title
Semi-supervised Learning (Adaptive Computation And Machine Learni
ISBN
9780262033589
Categoria

Informazioni su questo prodotto

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
Author
Alexander Zien
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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