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

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https://doi.org/10.1007/978-3-662-26042-5regions 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

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https://doi.org/10.1007/978-3-662-26042-5erfit noisy training data, and because of the sometimes impractically high number of trainable parameters. Much more popular are . where many simple units, called ., are interconnected by weighted links into larger structures of remarkably high performance.

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https://doi.org/10.1007/978-3-662-26042-5ws. Thus a physician seeking to come to grips with the nature of her patient’s condition often has nothing to begin with save a few subjective symptoms. And so, to narrow the field of diagnoses, she prescribes lab tests, and, based on the results, perhaps other tests still. At any given moment, then

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https://doi.org/10.1007/978-3-662-26042-5at 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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https://doi.org/10.1007/978-3-662-26042-5behind a textbook’s toy domains has a way of complicating things, frustrating the engineer with unexpected obstacles, and challenging everybody’s notion of what exactly the induced classifier is supposed to do and why. Just as in any other field of technology, success is hard to achieve without a he

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https://doi.org/10.1007/978-3-662-26042-5ge, 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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