Graph-based semi-supervised learning /

Bibliographic Details
Main Authors:	Subramanya, Amarnag (Author), Talukdar, Partha Pratim (Author)
Format:	eBook
Language:	English
Published:	San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:	Synthesis lectures on artificial intelligence and machine learning ; # 29.
Subjects:	Machine learning > Graphic methods. Supervised learning (Machine learning) graph Laplacian graph-based learning graph-based semi-supervised learning inductive learning label propagation manifold learning nonparametric learning scalable machine learning semi-supervised learning transductive learning Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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
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
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
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
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
6. Future work
6.1 Graph construction
6.2 Learning & inference
6.3 Scalability
A. Notations
B. Solving modified adsorption (MAD) objective
C. Alternating minimization
D. Software
D.1. Junto label propagation toolkit
Bibliography
Authors' biographies
Index.