强所 发表于 2025-3-23 13:27:28

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思想上升 发表于 2025-3-23 15:33:24

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唠叨 发表于 2025-3-23 19:58:22

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dermatomyositis 发表于 2025-3-24 00:29:42

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

易达到 发表于 2025-3-24 05:37:47

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古文字学 发表于 2025-3-24 07:17:36

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Eulogy 发表于 2025-3-24 12:05:08

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Affluence 发表于 2025-3-24 18:49:41

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 ..

并置 发表于 2025-3-24 21:40:15

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轻打 发表于 2025-3-25 02:49:23

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