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An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our improved kNN algorithm focuses on finding out the...
Ensemble learning plays an important role in pattern recognition. It combines multiple generated models to solve learning problems, such as classification, regression and feature selection. Existing ensemble methods combine classifiers which are generated and run in parallel. In this paper, a novel ensemble learning approach for semi-supervised learning is proposed. Different from existing ensemble...
Most semi-supervised learning methods assume there are a number of labeled data available in order to learn a classifier which then exploits a large set of unlabeled data. However, for some applications, there are only extremely spare labeled examples attainable (say, one example per category). In this case, these semi-supervised learning methods can not work. In this paper, a new method for seeking...
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