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Evaluation of the UNESCO World Heritage Sites (WHSs) in India has become a challenging activity for the tourism planners, policy makers and managers to create effective management strategies for them. In this paper, a combined application ofpreference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA) is presented to appraise the performance of37 Indian UNESCO WHSs based on nine important criteria. It is observed that the Taj Mahal is the most attractive site for both domestic and foreign tourists, followed by the Historic City of Ahmadabad. On the other hand, Champaner-Pavagadh Archaeological Park is the least visited site. Number of citations for a specific site in https://www.google.com appears to be the most important evaluation criterion, followed by the number of good accommodations. The application of this combined methodology would help the decision-makers to visualize the strengths and weaknesses of each of the Indian UNESCO WHSs so as to formulate suitable recommendations and policies to increase their tourism attractiveness. [ABSTRACT FROM AUTHOR]

Nearest neighbor classifiers that use all the training samples for classification require large memory and demand large online testing computation. To reduce the memory requirements and the computation cost, many algorithms have been developed that perform nearest neighbor classification using only a small number of representative samples obtained from the training set. We call the classification model underlying all these algorithms as Voronoi networks (Vnets), because these algorithms discretize the feature space into Voronoi regions and assign the samples in each region to a class. In this paper we analyze the generalization capabilities of these networks by bounding the generalization error. The class of problems that can be 'efficiently' solved by Vnets is characterized by the extent to which the set of points on the decision boundaries fill the feature space, thus quantifying how efficiently a problem can be solved using Vnets. We show that Vnets asymptotically converge to the Bayes classifier with arbitrarily high probability provided the number of representative samples grow slower than the square root of the number of training samples and also give the optimal growth rate of the number of representative samples. We redo the analysis for decision tree (DT) classifiers and compare them with Vnets. The bias/variance dilemma and the curse of dimensionality with respect to Vnets and DTs are also discussed. Index Terms--Neural networks, pattern recognition, statistical learning theory.