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

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STANT

https://doi.org/10.1007/978-3-662-65083-7n and negative examples in another. This motivates yet another machine-learning approach to classification: instead of the probabilities and similarities from the previous two chapters, the idea is to define a . that separates the two classes. This surface can be linear—and indeed, linear functions

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時(shí)代

https://doi.org/10.1007/978-3-642-94166-5e links that interconnect the neurons. The task for machine learning is to provide algorithms capable of finding weights that result in good classification behavior. This search is accomplished by a process commonly referred to as a neural network’s ..

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Rudolf Demel,Otto Hoche,Paul Moritschat it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking often disappoints the user. And so, even though this textbook does not want to be mathematical, it cannot help discussing at least the basic concepts of the ..

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木質(zhì)

https://doi.org/10.1007/978-3-7091-2188-7exchanging diverse points of view that complement each other in ways likely to inspire unexpected solutions. Something similar can be done in machine learning, as well. A group of classifiers is created, each of them somewhat different. When they vote about a class label, their “collective wisdom” o

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https://doi.org/10.1007/978-3-322-87998-1n class labels and then calculating the classifier’s error rate on these examples. In reality, however, error rate rarely paints the whole picture, and there are situations in which it is outright misleading. The reader needs to be acquainted with performance criteria that offer a more plastic view

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