Combining Pattern Classifiers, 2nd Edition: Methods and by Ludmila I. Kuncheva

By Ludmila I. Kuncheva

A unified, coherent therapy of present classifier ensemble equipment, from basics of development popularity to ensemble function choice, now in its moment version The artwork and technology of mixing trend classifiers has flourished right into a prolific self-discipline because the first variation of mixing development Classifiers used to be released in 2004. Dr. Kuncheva has plucked from the wealthy panorama of modern classifier ensemble literature the themes, equipment, and algorithms that may advisor the reader towards a deeper knowing of the basics, layout, and functions of classifier ensemble tools.

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Extra resources for Combining Pattern Classifiers, 2nd Edition: Methods and Algorithms

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Then the number of errors has a binomial distribution with parameters (PD , Nts ). An estimate of PD is P̂ D . If Nts and PD satisfy the rule of thumb: Nts > 30, P̂ D × Nts > 5, and (1 − P̂ D ) × Nts > 5, the binomial distribution can be approximated by a normal distribution. 96 ⎢ Nts Nts ⎣ ⎤ ⎥. 9) By calculating the confidence interval we estimate how well this classifier (D) will fare on unseen data from the same problem. Ideally, we will have a large representative testing set, which will make the estimate precise.

2 CLASSIFIER, DISCRIMINANT FUNCTIONS, CLASSIFICATION REGIONS A classifier is any function that will assign a class label to an object x: D : Rn → Ω. 9). The object x ∈ Rn is labeled to the class with the highest score. This labeling choice is called the maximum membership rule. Ties are broken randomly, meaning that x is assigned randomly to one of the tied classes. The discriminant functions partition the feature space Rn into c decision regions or classification regions denoted 1 , … , c : { } | n | i = x |x ∈ R , gi (x) = max gk (x) , i = 1, … , c.

9 Canonical model of a classifier. An n-dimensional feature vector is passed through c discriminant functions, and the largest function output determines the class label. The decision region for class ????i is the set of points for which the i-th discriminant function has the highest score. According to the maximum membership rule, all points in decision region i are assigned to class ????i . The decision regions are specified by the classifier D, or equivalently, by the discriminant functions G. The boundaries of the decision regions are called classification boundaries and contain the points for which the highest discriminant functions tie.

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