منابع مشابه
Boosting Local Consistency Algorithms over Floating-Point Numbers
Solving constraints over oating-point numbers is a critical issue in numerous applications notably in program veri cation. Capabilities of ltering algorithms over the oating-point numbers (F) have been so far limited to 2b-consistency and its derivatives. Though safe, such ltering techniques su er from the well known pathological problems of local consistencies, e.g., inability to e ciently han...
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Solving constraints over floating-point numbers is a critical issue in numerous applications notably in program verification. Capabilities of filtering algorithms for constraints over the floating-point numbers have been so far limited to 2b-consistency and its derivatives. Though safe, such filtering techniques suffer from the well known pathological problems of local consistencies, e.g., inab...
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Boosting is a kind of ensemble methods which produce a strong learner that is capable of making very accurate predictions by combining rough and moderately inaccurate learners (which are called as base learners or weak learners). In particular, Boosting sequentially trains a series of base learners by using a base learning algorithm, where the training examples wrongly predicted by a base learn...
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A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...
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ژورنال
عنوان ژورنال: British Dental Journal
سال: 2004
ISSN: 0007-0610,1476-5373
DOI: 10.1038/sj.bdj.4811778