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    STUDIA INFORMATICA - Issue no. 2 / 2008  
         
  Article:   ON SUPERVISED AND SEMI-SUPERVISED K-NEAREST NEIGHBOR ALGORITHMS.

Authors:  ZALÁN BODÓ, ZSOLT MINIER.
 
       
         
  Abstract:  The k-nearest neighbor (kNN) is one of the simplest classification methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this paper kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classification settings. Semi-supervision is performed by using data-dependent kernels.

Key words and phrases. Supervised learning, Semi-supervised learning, k-nearest neighbors, Data-dependent kernels.
 
         
     
         
         
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