Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bayesian classifiers.b

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Statistical Significance,mes up . eight times out of ten, any sensible person will say this is nothing but a fluke, easily refuted by new trials. Similar caution is in place when evaluating a classifier in machine learning. To measure its performance on a testing set is not enough; just as important is an estimate of the ch

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Induction in Multi-label Domains,he case. Quite often, an example is known to belong to two or more classes at the same time, sometimes to . classes. This situation presents certain new problems whose nature the engineer needs to understand.

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Deep Learning, fail. For another, the excessive detail of available attributes may obscure vital information about the data. To cope with these complications, more advanced techniques are needed. This is why . was born.

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Reinforcement Learning: ,-Armed Bandits and Episodes,ery practical. In this way, the computer can learn how to navigate a complicated maze, how to balance a broom-stick, and even how to drive a car or how to play complicated games such as chess or Go. The principle is to “learn from experience.” Facing diverse situations, the agent experiments, acts,

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https://doi.org/10.1007/978-3-030-81935-4Bayesian classifiers; boosting; computational learning theory; decision trees; genetic algorithms; linear

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Springer Nature Switzerland AG 2021

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Von Lebensgemeinschaften zum Metaorganismusew of her pictures, he will immediately see the tell-tale traits. As they say, a picture—an example—is worth a thousand words. Likewise, you will not become a professional juggler by just being told how to do it. The best any instructor can do is to offer some initial advice, and then let you practi