By Miroslav Kubat
This ebook provides easy rules of desktop studying in a manner that's effortless to appreciate, via delivering hands-on functional suggestion, utilizing uncomplicated examples, and motivating scholars with discussions of attention-grabbing functions. the most issues contain Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, choice bushes, neural networks, and help vector machines. Later chapters exhibit the right way to mix those easy instruments when it comes to “boosting,” find out how to take advantage of them in additional complex domain names, and the way to house diversified complex functional concerns. One bankruptcy is devoted to the preferred genetic algorithms.
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Extra resources for An Introduction to Machine Learning
3. Some classifiers behave as black boxes that do not offer much in the way of explanations. Such was the case of the “circles” domain. Suggest examples of domains where black-box classifiers are impractical, and suggest domains where this limitation does not matter. 18 1 A Simple Machine-Learning Task 4. ). Suggest the list of attributes to describe the training examples. Are the values of these attributes easy to obtain? Which of the problems discussed in this chapter do you expect will complicate the learning process?
The positive examples are contained in one circle and those with filling-size=thick in the other; the intersection contains three instances that satisfy both conditions; one pie satisfies neither, and is therefore left outside both circles. posjthick/ D 3=8, is obtained by dividing the size of the intersection (three) by the size of the circle thick (eight). pos; thick/ D 3=12, is obtained by dividing the size of the intersection (three) by the size of the entire training set (twelve). pos/ D 6=12 is obtained by dividing the size of the circle pos (six) with that of the entire training set (twelve).
1 Illustrating the principle of Bayesian decision making Let the training examples be described by a single attribute, filling-size, whose value is either thick or thin. We want the machine to recognize the positive class (pos). Here are the eight available training examples: Size Class ex1 thick pos ex2 thick pos ex3 thin pos ex4 thin pos ex5 thin neg ex6 thick neg ex7 thick neg ex8 thick neg The probabilities of the individual attribute values and class labels are obtained by their relative frequencies.
An Introduction to Machine Learning by Miroslav Kubat