Big-Data Analytics for Cloud, IoT and Cognitive Computing

4 Supervised Machine Learning Algorithms

CHAPTER OUTLINE

1. 4.1 Taxonomy of Machine Learning Algorithms
   1. 4.1.1 Machine Learning Based on Learning Styles
   2. 4.1.2 Machine Learning Based on Similarity Testing
   3. 4.1.3 Supervised Machine Learning Algorithms
   4. 4.1.4 Unsupervised Machine Learning Algorithms
2. 4.2 Regression Methods for Machine Learning
   1. 4.2.1 Basic Concepts of Regression Analysis
   2. 4.2.2 Linear Regression for Prediction and Forecast
   3. 4.2.3 Logistic Regression for Classification
3. 4.3 Supervised Classification Methods
   1. 4.3.1 Decision Trees for Machine Learning
   2. 4.3.2 Rule-based Classification
   3. 4.3.3 The Nearest Neighbor Classifier
   4. 4.3.4 Support Vector Machines
4. 4.4 Bayesian Network and Ensemble Methods
   1. 4.4.1 Bayesian Classifiers
   2. 4.4.2 Bayesian Belief Networks
   3. 4.4.3 Random Forests and Ensemble Methods
5. 4.5 Conclusions

4.1 Taxonomy of Machine Learning Algorithms

Machine learning (ML) is an actionable discipline extended from the study of pattern recognition and computational learning theory in artificial intelligence (AI). This field is highly relevant to statistical decision making and data mining in building AI or expert systems. The key idea is to use computers to learn from data. For tedious or unstructured data, machines can often make better and more unbiased decisions than the human learner. Toward this end, we need to write a computer program based on a model algorithm. Learning from given data objects, we can reveal the categorical class or experience affiliation ...