- Using Matlab to understand all the algorithm introduced in this book.
- All code here generate some figures to comprehend the procedure clearly.
- Supervised Regression
- Chapter 03 Least Squares Regression example 3.1 example 3.2 gif
- Chapter 04 Constrained Least Squares Regression example 4.1 example 4.2 example 4.3
- Chapter 05 LASSO and Ridge Regression example 5.1
- Chapter 06 Robust Regression example 6.1 example 6.2 example 6.3
- Supervised Classification
- Chapter 07 Least Squares Classification example 7.1
- Chapter 08 Support Vector Machine example 8.1 example 8.2
- Chapter 09 Bagging and Adaboost example 9.1 example 9.2 example 9.3
- Chapter 10 Logistic Regression and Least-Squares Probabilistic Classifierand example 10.1 gif example 10.2
- Chapter 11 Conditional Random Field (no examples provided)
- Unsupervised Learning
- Chapter 12 Anomaly Detection example 12.1 example 12.2
- Chapter 13 Unsupervised Dimension Reduction example 13.1 example 13.2 example 13.3
- Chapter 14 Cluster example 14.1 example 14.2 example 14.3
- Recently Emergent Algorithms
- Chapter 15 Online Machine Learning example 15.1 example 15.2
- Chapter 16 Semi-supervised Learning example 16.1
- Chapter 17 Supervised Dimension Reduction example 17.1 example 17.2
- Chapter 18 Transfer Learning example 18.1 example 18.2
Use this code whatever you want, under the circumstances of acknowleged the mit license this page below. Star this repository if you like, and it will be very generous of you!
The MIT License (MIT) Copyright (c) 2016 Shangkun Shen, http://polossk.com polossk_dev@126.com
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