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By Zhi-Hua Zhou

Advent easy suggestions renowned studying Algorithms review and comparability Ensemble tools functions of Ensemble equipment Boosting A basic Boosting method The AdaBoost set of rules Illustrative Examples Theoretical concerns Multiclass Extension Noise Tolerance Bagging Ensemble Paradigms The Bagging set of rules Illustrative Examples Theoretical matters Random Tree Ensembles mix tools advantages of mix Averaging vote casting Combining through studying different mixture tools appropriate equipment variety Ensemble range errors Decomposition range Measures details Theoretic range variety new release Ensemble Pruning what's Ensemble Pruning Many may be greater Than All Categorization of Pruning tools Ordering-Based Pruning Clustering-Based Pruning Optimization-Based Pruning Clustering Ensembles Clustering Categorization of Clustering Ensemble tools Similarity-Based equipment Graph-Based tools Relabeling-Based equipment Transformation-Based tools complex themes Semi-Supervised studying lively studying Cost-Sensitive studying Class-Imbalance studying bettering Comprehensibility destiny instructions of Ensembles References Index extra Readings seem on the finish of every bankruptcy.

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If there is more than one basis function with the smallest error, it selects one randomly. Notice that none of these eight basis functions can separate the two classes. Now we track how AdaBoost works: 1. The first step is to invoke the base learning algorithm on the original data. 25, suppose the base learning algorithm outputs h2 as the classifier. 5) +1, otherwise where x1 and x2 are the values of x at the first and the second dimension, respectively. 4: The eight basis functions considered by the base learning algorithm.

However, Breiman [1999] found in experiments that, though arc-gv does produce uniformly larger minimum margin than AdaBoost, the test error of arc-gv increases drastically in almost every case. Hence, Breiman [1999] convincingly concluded that the margin-based explanation for AdaBoost was in serious doubt and a new understanding is needed. This almost sentenced the margin theory to death. Seven years later, Reyzin and Schapire [2006] reported an interesting finding. 20) is relevant to the margin, the number of learning rounds and the complexity of base learners.

In the latter case there is no single base learning algorithm and thus, some people prefer calling the learners individual learners or component learners to base learners. The generalization ability of an ensemble is often much stronger than that of base learners. Actually, ensemble methods are appealing mainly because they are able to boost weak learners which are even just slightly better than random guess to strong learners which can make very accurate predictions. So, base learners are also referred to as weak learners.

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