Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 20172nd edition Springer International Publishing AG 2017 Bayesian classifier

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Textbook 20172nd editionmples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way

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https://doi.org/10.1007/978-3-8350-9083-5at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the ..

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Die Unbeherrschtheit bei Platonge, offering diverse points of view that complement each other to the point where they may inspire innovative solutions. Something similar can be done in machine learning, too. A group of classifiers is created in a way that makes each of them somewhat different. When they vote about the recommended