Graph-based semi-supervised learning /

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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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Subramanya, Amarnag (Autor), Talukdar, Partha Pratim (Autor)
Format: E-book
Język:English
Wydane: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Seria:Synthesis digital library of engineering and computer science.
Synthesis lectures on artificial intelligence and machine learning ; # 29.
Hasła przedmiotowe:
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
Dostęp online:Abstract with links to full text
Spis treści:
  • 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.

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