By Miroslav Kubat
This e-book offers uncomplicated principles of desktop studying in a manner that's effortless to appreciate, through delivering hands-on useful recommendation, utilizing easy examples, and motivating scholars with discussions of fascinating purposes. the most issues comprise Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, selection timber, neural networks, and help vector machines. Later chapters express the right way to mix those uncomplicated instruments in terms of “boosting,” the right way to take advantage of them in additional advanced domain names, and the way to accommodate assorted complicated sensible concerns. One bankruptcy is devoted to the preferred genetic algorithms.
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Additional info for An Introduction to Machine Learning
B1 b2 b3 b4 b5 b6 b7 b8 x p(x) x Fig. 2 A simple discretization method that represents each subinterval by a separate bin. The bottom chart plots the histogram over the individual subintervals p(x) Gaussian "bell" function p a b age Fig. 3 When using the pdf, we identify the probability of x 2 Œa; b with the relative size of the area below the corresponding section of the pdf infinitesimally small. x/, such as the one in Fig. 3. x/ tells us that age in the vicinity of x is rare. x/ a probability density function, often avoiding this mouthful by preferring the acronym pdf.
What Have You Learned? To make sure you understand the topic, try to answer the following questions. If needed, return to the appropriate place in the text. • How is the Bayes formula derived from the relation between the conditional and joint probabilities? • What makes the Bayes formula so useful? What does it enable us to calculate? xjy/? x; y/? x1 ; x2 ; : : : ; xn /. 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.
This is the essence of the so-called k-NN classifier, where k is the number of the voting neighbors—usually a user-specified parameter. 2 summarizes the algorithm. Note that, in a two-class domain, k should be an odd number so as to prevent ties. For instance, a 4-NN classifier might face a situation where the number of positive neighbors is the same as the number of negative neighbors. This will not happen to a 5-NN classifier. As for domains that have more than two classes, using an odd number of nearest neighbors does not prevent ties.