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

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PSA-velocity

Similarities: Nearest-Neighbor Classifiers,rom the same disease. Similar objects often belong to the same class—an observation underlying another popular approach to classification: when asked to determine the class of object ., find the training example most similar to it, and then label . with this similar example’s class.

Pert敏捷

Inter-Class Boundaries: Linear and Polynomial Classifiers,n 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

協(xié)迫

Decision Trees,e attribute vector that describes the example. In some applications, this scenario is unrealistic. A physician looking for the cause of her patient’s ailment may have nothing to begin with save a few subjective symptoms. To narrow the field of possible diagnoses, she prescribes a few lab tests, and,

investigate

Artificial Neural Networks,e 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 ..

指派

Computational Learning Theory,at 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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Experience from Historical Applications,s has a way of complicating things, frustrating the engineer with unexpected hurdles, and challenging everybody’s notion of what exactly the induced classifier is supposed to do and why. Just as everywhere in the world of technology, a healthy dose of creativity is indispensable.

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Voting Assemblies and Boosting,exchanging 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

CYT

Classifiers in the Form of Rule-Sets,fied by the .-part. The advantage is that the rule captures the logic underlying the given class and thus facilitates an explanation of why an example is to be labeled in this or that concrete way. Typically, a classifier of this kind is represented not by a single rule, but by a set of rules, a ..

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