C02-1090 found to be the best version of kNN . Category-based method . To
C02-1090 The algorithm did not improve on kNN in terms of direct hits . The
C02-1090 statistically significant improvement on kNN overall , or in any of the frequency
C02-1090 describes results of evaluation of kNN using 30 nearest neighbors ,
C02-1090 down the search space . Then the kNN method was applied to pick a
C02-1090 score was on par with that of kNN ! This algorithm thus produced
C02-1090 classification algorithm which extends the kNN method by making use of the taxonomic
C02-1090 tree ascending algorithm with kNN in one algorithm in the following
C02-1090 twice as few direct hits than kNN . At the same time , its di -
C02-1090 number of nearest neighbors as with kNN . Table 6 describes the results
C02-1072 spaces is required when performing kNN search to select the translation
C02-1090 the one-tailed chisquare test . kNN . Evaluation of the method was
C02-1090 category-based method improves on kNN ( L1 , p < 0.001 ) . The centroid-based
C02-1090 ranges : for lower frequencies kNN is more accurate ( e.g. , for
C02-1090 combinations with other algorithms like kNN . The tree descending algorithm
C02-1090 defines membership in a class . The kNN method is based on the assumption
C02-1090 search space and then apply the kNN method to determine the correct
C00-1022 provides several extensions to kNN , well-suited tbr NLP 1 ) rol
C00-1022 K-nearest neighbors algorithm ( kNN ) , mid then ( ; lie TiMBL learner
C02-1090 ( Figure 1 ) . In this case , kNN will classify trailer into the
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