Titlebook: Machine Learning Foundations; Supervised, Unsuperv Taeho Jo Book 2021 Springer Nature Switzerland AG 2021 Machine Learning.Supervised Learn

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Advanced Clusteringention the clustering governance as the management and the maintenance of existing clusters and, in Sect. 12.5, make the summarization on this chapter and the further discussions. This chapter covers the clustering evaluation metric, the clustering algorithms with their parameter turning, and the cl

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Temporal Learningrobabilistic model, called discrete Markov model. In Sect. 15.3, we describe the Hidden Markov Model as the temporal learning algorithm with the three problems. In Sect. 15.4, we mention the application of the Hidden Markov Model to the text topic analysis, and in Sect. 15.5, we make the summarizati

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Increment

Support Vector Machinen Sect. 8.4 and make the summarization on this chapter and the further discussions in Sect. 8.5. This chapter is intended to describe the SVM entirely with respect to the classification process, the learning process, and the variants.

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Mechanics

Obvious

Introductione able to apply the machine learning algorithms in the functional view. We describe briefly the four types of machine learning algorithms: the supervised learning, the unsupervised one, the reinforced one, and the semi-supervised one. We explore other areas which are related with the current area, c

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