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AN INCREMENTAL LEARNER VIA AN ADAPTIVE MIXTURE OF WEAK LEARNERS DISTRIBUTED ON A NON-RIGID BINARY TREE
AN INCREMENTAL LEARNER VIA AN ADAPTIVE MIXTURE OF WEAK LEARNERS DISTRIBUTED ON A NON-RIGID BINARY TREE
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机译:通过分布在非刚性二叉树上的弱学习者的自适应混合来增加学习者
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摘要
The present invention relates to a method for incremental learning of a classification model, where pre-defined weak incremental learners are distributed over the distinct regions in a set of partitionings of the input domain. The partitionings and regions are organized via a binary tree and they are allowed to vary in a data-driven way, i.e., in a way to minimize the classification error rate. Moreover, to test a given data point, a mixture of decisions is obtained through the models learned in the regions that this point falls in. Hence, naturally, in the cold start phase of the data stream, the simpler models belonging to the larger regions are favored and as more data get available, the invention automatically puts more weights on the more complex models.
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