Graph-Based Semi-Supervised Learning (Record no. 84816)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03622nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01571-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163630.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2014 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031015717 |
| -- | 978-3-031-01571-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Subramanya, Amarnag. |
| 245 10 - TITLE STATEMENT | |
| Title | Graph-Based Semi-Supervised Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2014. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 111 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Artificial Intelligence and Machine Learning, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Graph Construction -- Learning and Inference -- Scalability -- Applications -- Future Work -- Bibliography -- Authors' Biographies -- Index . |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Talukdar, Partha Pratim. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01571-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2014. |
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| -- | text |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical Models of Cognitive Processes and Neural Networks. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1939-4616 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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