By Ludmila I. Kuncheva
A unified, coherent therapy of present classifier ensemble tools, from basics of trend reputation to ensemble characteristic choice, now in its moment version The paintings and technological know-how of mixing trend classifiers has flourished right into a prolific self-discipline because the first version of mixing trend Classifiers was once released in 2004. Dr. Kuncheva has plucked from the wealthy panorama of modern classifier ensemble literature the subjects, equipment, and algorithms that may consultant the reader towards a deeper knowing of the basics, layout, and functions of classifier ensemble tools.
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Additional resources for Combining Pattern Classifiers, 2nd Edition: Methods and Algorithms
The quantity of interest is called the generalization error. This is the expected error of the trained classifier on unseen data drawn from the distribution of the problem. 1 Where Does the Error Come From? Bias and Variance Why cannot we design the perfect classifier? 11 shows a sketch of the possible sources of error. Suppose that we have chosen the classifier model. Even with a perfect training algorithm, our solution (marked as 1 in the figure) may be away from the best solution with this model (marked as 2).
The second term, PM , can be taken as the bias of the model from the best possible solution. 12. We can liken building the perfect classifier to shooting at a target. Suppose that our training algorithm generates different solutions owing to different data samples, different initialisations, or random branching of the training algorithm. 12 Bias and variance. grouped together, variance is low. Then the distance to the target will be more due to the bias. Conversely, widely scattered solutions indicate large variance, and that can account for the distance between the shot and the target.
Suppose that our training algorithm generates different solutions owing to different data samples, different initialisations, or random branching of the training algorithm. 12 Bias and variance. grouped together, variance is low. Then the distance to the target will be more due to the bias. Conversely, widely scattered solutions indicate large variance, and that can account for the distance between the shot and the target. 2 Estimation of the Error Assume that a labeled data set Zts of size Nts × n is available for testing the accuracy of our classifier, D.