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Introduction
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Classroom Contents
Graph Constructions for Machine Learning Applications - New Insights and Algorithms
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- 1 Introduction
- 2 Motivation
- 3 Outline
- 4 Basic definitions
- 5 Graph signal variation
- 6 Semisupervised learning
- 7 Graph signal sampling
- 8 Active semisupervised learning
- 9 Graph signal variations
- 10 Paper
- 11 Theoretical Analysis
- 12 Conventional Approach
- 13 orthogonalization
- 14 linear embeddings
- 15 label propagation
- 16 deep neural networks
- 17 supervised classification
- 18 smoothness
- 19 regularization
- 20 local political interpolation
- 21 local nonparametric approach
- 22 motor model selection
- 23 local interpolation
- 24 Geometry of deep learning
