Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 20151st edition Springer International Publishing Switzerland 2015 Applicatio

歌劇等

CRANK

vibrant

Inter-Class Boundaries: Linear and Polynomial Classifiers,regions different from those occupied by negative examples. This observation motivates yet another approach to classification. Instead of the probabilities and similarities employed by the earlier paradigms, we can try to identify the . that separates the two classes. A very simple possibility is to

BROW

Condescending

Kidney-Failure

Computational Learning Theory,at 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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Tractable

Induction of Voting Assemblies,ge, 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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Countermand

Statistical Significance,omes up . eight times out of ten, any reasonable experimenter will suspect this to be nothing but a fluke, expecting that another set of ten tosses will give a result closer to reality. Similar caution is in place when measuring classification performance. To evaluate classification accuracy on a te