大數據中極端問題的人工智能解決方案(英文)

CHAPTER 1 Basics of Machine Learning
1.1 Problem statement and solution framework
1.2 Supervised learning
1.2.1 MLP
1.2.2 CNN
1.2.3 RBF network
1.2.4 SVM
1.2.5 Comments
1.3 Unsupervised learning
1.3.1 K-means
1.3.2 Self-organizing map
1.3.3 Comments
1.4 Representation learning
1.4.1 PCA
1.4.2 LDA
1.4.3 ICA
1.4.4 NMF
1.4.5 Comments
References

CHAPTER 2 Solving Multi-class Problems by Data-driven Topology-preservingOutput Codes
2.1 Think: Is complexity important?
2.2 Topology-preserving output code scheme
2.2.1 A first-place description
2.2.2 Definition of a TPOC map
2.2.3 TOP map learned from SOM
2.2.4 Learning algorithm for a TPOC map
2.2.5 An octa-phase-shift-keying (8-PSK) pattern example
2.3 Experimental results
2.3.1 Comparison of TPOC with DECOC
2.3.2 Comparison of TPOC with OAA
2.3.3 Comparison of TPOC with random code and natural code
2.3.4 Comparison of TPOC with q-TPOC scheme and ECOC scheme
2.3.5 Comparison of TPOC schemes with and without adaptive assignment of classifier complexity
2.3.6 Measured radar data classification with multiple SVM
2.4 Discussions
2.4.1 Advantages of TPOC over ECOC
2.4.2 Relation of TPOC to other related approaches
2.5 Summary
Appendix Coding classes from a TPOC map
Appendix 1 k-ary coding scheme: Using k-ary classifiers
Appendix 2 Binary coding scheme: Using binary classifiers
References

CHAPTER 3 Robust Data Clustering by Learning Multi-metric Lq-norm Distances
3.1 Why distance measure is important?
3.2 Motivation for robust multi-metric clustering
3.3 Robust location estimation
3.3.1 RMML algorithm
3.3.2 Objective function
3.3.3 Non-Gaussianity measure of a mapped cluster
3.4 Robust outlier detection: ICSC algorithm
3.5 Experiments and results
3.5.1 Location estimation on alpha-stable mixture datasets
3.5.2 Comparisons of proposed RMML algorithm with typical robust clustering algorithms
3.5.3 Outlier detection on R-data and D-data
3.5.4 Experiments on Wisconsin Breast Cancer Dataset and on Lung Cancer Dataset
3.6 Discussions
3.7 Summary
Appendix 1 CDM algorithm
Appendix 2 Proof of Theorem 3.1
References

CHAPTER 4 Minimum Resource Neural Network Framework for SolvingMulti-constraint Shortest Path Problems
4.1 Introduction
4.2 MRNN for solving time constraint shortest time path problems
4.2.1 Problem definitions
4.2.2 Neural network design
4.2.3 Algorithm for solving the ST-TW problem
4.2.4 Flexibility of the network
4.2.5 Properties of the network
4.3 MRNN for solving label-constraint shortest path problem
4.4 Computation complexity analysis
4.5 Experiments and results
4.5.1 Experiments on simulated data
4.5.2 Experiments on real city road maps
4.5.3 Experiments on vehicle routing problem with time windows
4.6 Summary
Appendix Proof of properties of the TW-TW network
References

CHAPTER 5 Overall-Regional Competitive Self-Organizing Map for EuclideanTraveling Salesman Problem
5.1 Introduction
5.2 ORC-SOM neural network
5.2.1 Overall competition and regional competition: idea
5.2.2 Overall competition and regional competition: formation
5.2.3 ORC-SOM algorithm for the Euclidean TSP
5.3 Feasibility analysis
5.3.1 Neighborhood preservation and convex-hull properties
5.3.2 Infiltration property
5.4 Experiments and results
5.5 Summary
References

CHAPTER 6 Filtering Images Contaminated with Pep and Salt Type Noise with Pulse-coupled Neural Network
6.1 Introduction
6.2 PCNN model and its dynamic behaviour
6.2.1 Dynamics of an isolated neuron
6.2.2 Dynamics of connected neurons
6.3 Localization and filtering of noisy pixels
6.3.1 Basic idea