Titlebook: Deep Learning Foundations; Taeho Jo Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature

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Supervised Learningameters are optimized for minimizing the error between the desired output and the computed one. In the supervised learning process, the training examples, each of which is labeled with its own target output, and the given learning algorithm are trained with them. Supervised learning algorithms are a

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Ensemble Learninging the problems such as classification, regression, and clustering, more reliably by combining multiple machine learning algorithms with each other. The typical schemes of ensemble learning are the voting which is the process of deciding the final answer by considering ones of multiple machine lear

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Deep Linear Classifierfines its dual parallel hyperplanes with the maximal margin between them as the classification boundary. Even if the SVM is viewed as a deep learning algorithm, compared with the simple linear classifier, by itself, it may be modified into its further deep versions by attaching the input encoding an

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Multiple Layer Perceptrony Rosenblatt in the 1950s. In the architecture of MLP, there are three layers: the input layer, the hidden layer, and the output layer. A layer is connected to its next layer with the feedforward direction, and the weights are updated in its learning process in the backward direction. This chapter i

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