منابع مشابه
Multi-class Boosting
This paper briefly surveys existing methods for boosting multi-class classication algorithms, as well as compares the performance of one such implementation, Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME), against that of Softmax Regression, Classification and Regression Trees, and Neural Networks.
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Boosting approaches are based on the idea that high-quality learning algorithms can be formed by repeated use of a “weak-learner”, which is required to perform only slightly better than random guessing. It is known that Boosting can lead to drastic improvements compared to the individual weak-learner. For two-class problems it has been shown that the original Boosting algorithm, called AdaBoost...
متن کاملMulti-Class Deep Boosting
We present new ensemble learning algorithms for multi-class classification. Our algorithms can use as a base classifier set a family of deep decision trees or other rich or complex families and yet benefit from strong generalization guarantees. We give new data-dependent learning bounds for convex ensembles in the multiclass classification setting expressed in terms of the Rademacher complexiti...
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The acquisition of face images is usually limited due to policy and economy considerations, and hence the number of training examples of each subject varies greatly. The problem of face recognition with imbalanced training data has drawn attention of researchers and it is desirable to understand in what circumstances imbalanced data set affects the learning outcomes, and robust methods are need...
متن کاملMulti-class Boosting with Class Hierarchies
We propose AdaBoost.BHC, a novel multi-class boosting algorithm. AdaBoost.BHC solves a C class problem by using C− 1 binary classifiers defined by a hierarchy that is learnt on the classes based on their closeness to one another. It then applies AdaBoost to each binary classifier. The proposed algorithm is empirically evaluated with other multi-class AdaBoost algorithms using a variety of datas...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2007
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-007-5005-y