Download e-book for iPad: Pattern Classification Using Ensemble Methods by Lior Rokach

By Lior Rokach

ISBN-10: 9814271063

ISBN-13: 9789814271066

Researchers from quite a few disciplines resembling development attractiveness, records, and desktop studying have explored using ensemble method because the past due seventies. hence, they're confronted with a wide selection of tools, given the transforming into curiosity within the box. This publication goals to impose a level of order upon this range by means of featuring a coherent and unified repository of ensemble equipment, theories, tendencies, demanding situations and purposes. The booklet describes intimately the classical equipment, in addition to the extensions and novel ways constructed lately. besides algorithmic descriptions of every technique, it additionally explains the conditions during which this technique is appropriate and the implications and the trade-offs incurred through the use of the strategy.

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Breiman (1996) notes that this is true especially for unstable inducers since bagging can eliminate their instability. In this context, an inducer is considered unstable if perturbations in the learning set can produce significant changes in the constructed classifier. 4 The Boosting Algorithm Boosting is a general method for improving the performance of a weak learner. The method works by iteratively invoking a weak learner, on training data that is taken from various distributions. Similar to bagging, the classifiers are generated by resampling the training set.

Instances whose weights were increased are depicted by larger symbols. The horizontal line depicts the decision boundary that was added by the second classifier. 36 Pattern Classification Using Ensemble Methods ÿ ➠ ✌ ✎ ✑ ✕ ✖ ✗ ✛ ÿ ✛ ✩ ✩ ✹ ❜ ➌ ❙ ➺ ❙ ❚ ♥ ◆ ❍ ▼ ❑ ◆ ❬ ❙ ❘ ♦ ❉ ✸ ✓ ❋ ✢ ✤ ❋ ✢ ✦ ✦ ✦ ✢ ✢ ✤ ✩ ❺ ❪ ❙ ♦ ♦ ❚ ➋ ❘ ❨ ◆ ♠ ➠ ✌ ✎ ➢ ü ❍ ✕ ü ü ❏ ❬ ❙ ❘ ❜ ✼ ⑧ ❋ ➏ ✼ ❫ ▼ ✿ ÿ ÿ ✗ ✛ ❚ ❙ ▼ ❑ ❲ ❲ ❲ ➏ ✗ ▲ ✘ ✎ ✽ ✎ ✢ ❚ ✤ ✦ ❘ ✦ ✦ ❘ ❘ ❉ ➡ ❨ ❩ ❭ ❫ ❜ ✗ ❩ ✘ ✢ ✦ ✦ ✦ ✢ ❺ ÿ ❑ ❝ ▼ ❑ ❬ ❘ ❨ ❲ ❑ ♦ ♦ ❬ ➯ ❍ ▼ ❙ ✬ ❘ ✙ ❑ ❘ ➧ ❙ ➑ ◆ ❑ ❬ ❘ ❢ ➏ ❏ ❙ ◆ ❢ ❍ ♦ ❬ ♦ ❁ ❂ ❄ ④ ⑧ ❆ ✖ ✇ ➀ ➀ ✢ ✈ ✇ ÿ ➑ ➄ ❍ ◆ ❑ ▼ ❫ P ❬ ❘ ❈ ❊ ● ④ ✂ ✗ ☎ ➏ ⑩ ➀ ✇ ✇ ✇ ❋ ÿ ❨ ➍ ❏ ➧ ❑ ◆ ④ ❍ ➏ ✗ ❑ ❘ € ➧ ❫ ▼ ➏ ◆ ✇ ➂ ✗ ✿ ÿ ÿ ✛ ❲ ✗ ➏ ✘ ❋ ⑩ ❋ ❘ ✇ ➠ ➎ ✓ ➠ ✑ ✓ ✕ ➏ ➂ ✇ ✇ ✇ ✢ ❘ ✇ ✤ ✢ ❚ ✢ ✦ ✦ ✦ ✢ ❘ ➂ ➐ Fig.

The first theory relates ensemble methods such as Adaboost to logistic regression. The decrease in misclassification rate of the ensemble is seen as a by-product of the likelihood’s improvement. The second theory refers to the large margins theory. Like in the theory of support vector machines (SVM), the focus of large margin theory is on the task of reducing the classification error rate on the test set. 9 Classifier Dependency Ensemble methods can be differentiated according to which extent each classifier affect the other classifiers.

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Pattern Classification Using Ensemble Methods by Lior Rokach


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