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

Intuitive

微塵

Numerical Vectors the summarization on this chapter and the further discussions. This chapter is intended to characterize mathematically vectors and matrices as the foundation for understanding machine learning algorithms.

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Instance Based LearningNN. We also present the modified versions of KNN as its variants. In this chapter, we assume that the KNN is the supervised learning algorithm, but we cover its unsupervised version in the next part..In Sect. 5.1, we provide the overview of the instance based learning, and in Sect. 5.2, we mention t

航海太平洋

Probabilistic Learningo provide the background for understanding the chapter. We describe in detail some probabilistic classifiers such as Bayes Classifier and Naive Bayes as the popular and simple machine learning algorithms. We cover the Bayesian Learning as the more advanced learning methods than the two probabilistic

神經

Decision Treemples, entirely. In Sect. 7.4, we mention some variants of the decision tree, and in Sect. 7.5, we make the summarization on this chapter and the further discussions. This chapter is intended to describe the classification process, the learning process, and the variants of the decision tree.

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小隔間

K Means Algorithme k means algorithm. In Sect. 10.4, we mention the variants of the k means algorithm, and in Sect. 10.5, we make the summarization on this chapter and the further discussions. This chapter is intended to describe the clustering process and the variants of the k means algorithm.

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