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Graph-based semi-supervised learning /
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...
| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2014.
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| Series: | Synthesis digital library of engineering and computer science.
Synthesis lectures on artificial intelligence and machine learning ; # 29. |
| Subjects: | |
| Online Access: | Abstract with links to full text |
| LEADER | 05941nam a2200757 i 4500 | ||
|---|---|---|---|
| 001 | 201408AIM029 | ||
| 005 | 20161114165121.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 140816s2014 caua foab 001 0 eng d | ||
| 020 | |a 9781627052023 |q ebook | ||
| 020 | |z 9781627052016 |q print | ||
| 024 | 7 | |a 10.2200/S00590ED1V01Y201408AIM029 |2 doi | |
| 035 | |a (CaBNVSL)swl00403736 | ||
| 035 | |a (OCoLC)887483734 | ||
| 040 | |a CaBNVSL |b eng |e rda |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a Q325.75 |b .S824 2014 | |
| 082 | 0 | 4 | |a 006.31 |2 23 |
| 100 | 1 | |a Subramanya, Amarnag., |e author. | |
| 245 | 1 | 0 | |a Graph-based semi-supervised learning / |c Amarnag Subramanya, Partha Pratim Talukdar. |
| 264 | 1 | |a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c 2014. | |
| 300 | |a 1 PDF (xiii, 111 pages) : |b illustrations. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a Synthesis lectures on artificial intelligence and machine learning, |x 1939-4616 ; |v # 29 | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 500 | |a Series from website. | ||
| 504 | |a Includes bibliographical references (pages 97-108) and index. | ||
| 505 | 0 | |a 1. Introduction -- 1.1 Unsupervised learning -- 1.2 Supervised learning -- 1.3 Semi-supervised learning (SSL) -- 1.4 Graph-based semi-supervised learning -- 1.4.1 Inductive vs. transductive SSL -- 1.5 Book organization -- | |
| 505 | 8 | |a 2. Graph construction -- 2.1 Problem statement -- 2.2 Task-independent graph construction -- 2.2.1 K-nearest neighbor (k-NN) and -neighborhood methods -- 2.2.2 Graph construction using b-matching -- 2.2.3 Graph construction using local reconstruction -- 2.3 Task-dependent graph construction -- 2.3.1 Inference-driven metric learning (IDML) -- 2.3.2 Graph kernels by spectral transform -- 2.4 Conclusion -- | |
| 505 | 8 | |a 3. Learning and inference -- 3.1 Seed supervision -- 3.2 Transductive methods -- 3.2.1 Graph cut -- 3.2.2 Gaussian random fields (GRF) -- 3.2.3 Local and global consistency (LGC) -- 3.2.4 Adsorption -- 3.2.5 Modified adsorption (MAD) -- 3.2.6 Quadratic criteria (QC) -- 3.2.7 Transduction with confidence (TACO) -- 3.2.8 Information regularization -- 3.2.9 Measure propagation -- 3.3 Inductive methods -- 3.3.1 Manifold regularization -- 3.4 Results on benchmark SSL data sets -- 3.5 Conclusions -- | |
| 505 | 8 | |a 4. Scalability -- 4.1 Large-scale graph construction -- 4.1.1 Approximate nearest neighbor -- 4.1.2 Other methods -- 4.2 Large-scale inference -- 4.2.1 Graph partitioning -- 4.2.2 Inference -- 4.3 Scaling to large number of labels -- 4.4 Conclusions -- | |
| 505 | 8 | |a 5. Applications -- 5.1 Text classification -- 5.2 Phone classification -- 5.3 Part-of-speech tagging -- 5.4 Class-instance acquisition -- 5.5 Knowledge base alignment -- 5.6 Conclusion -- | |
| 505 | 8 | |a 6. Future work -- 6.1 Graph construction -- 6.2 Learning & inference -- 6.3 Scalability -- | |
| 505 | 8 | |a A. Notations -- B. Solving modified adsorption (MAD) objective -- C. Alternating minimization -- D. Software -- D.1. Junto label propagation toolkit -- Bibliography -- Authors' biographies -- Index. | |
| 506 | |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | |a Compendex | |
| 510 | 0 | |a Google book search | |
| 510 | 0 | |a Google scholar | |
| 510 | 0 | |a INSPEC | |
| 520 | 3 | |a 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. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 538 | |a System requirements: Adobe Acrobat Reader. | ||
| 588 | |a Title from PDF title page (viewed on August 16, 2014). | ||
| 650 | 0 | |a Machine learning |x Graphic methods. | |
| 650 | 0 | |a Supervised learning (Machine learning) | |
| 653 | |a graph Laplacian | ||
| 653 | |a graph-based learning | ||
| 653 | |a graph-based semi-supervised learning | ||
| 653 | |a inductive learning | ||
| 653 | |a label propagation | ||
| 653 | |a manifold learning | ||
| 653 | |a nonparametric learning | ||
| 653 | |a scalable machine learning | ||
| 653 | |a semi-supervised learning | ||
| 653 | |a transductive learning | ||
| 700 | 1 | |a Talukdar, Partha Pratim., |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781627052016 |
| 830 | 0 | |a Synthesis digital library of engineering and computer science. | |
| 830 | 0 | |a Synthesis lectures on artificial intelligence and machine learning ; |v # 29. |x 1939-4616 | |
| 856 | 4 | 8 | |3 Abstract with links to full text |u http://dx.doi.org/10.2200/S00590ED1V01Y201408AIM029 |
| 942 | |c EB | ||
| 999 | |c 114276 |d 114276 | ||
| 952 | |0 0 |1 0 |4 0 |7 0 |9 111036 |a MGUL |b MGUL |d 2016-11-14 |l 0 |r 2016-11-14 |w 2016-11-14 |y EB | ||