Abstract
This chapter explains basic machine learning technologies other than regression as methods for hypothesis generation and then introduces integrated methods for hypothesis generation as follows.
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Explain clustering and association rule mining as examples of unsupervised learning.
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Explain neural networks and deep learning as examples of supervised learning.
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Explain integrated methods for generating hypotheses by combining multiple data and multiple hypotheses.
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Ishikawa, H. (2024). Machine Learning and Integrated Approach. In: Hypothesis Generation and Interpretation. Studies in Big Data, vol 139. Springer, Cham. https://doi.org/10.1007/978-3-031-43540-9_5
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