In this part, you will implement k-Nearest Neighbor (k-NN) algorithm on the 8scenes category dataset of Oliva and Torralba [1]. You are given a total of 800 labeled training images (containing 100 images for each class) and 1888 unlabeled testing images. Figure 1 shows some sample images from the data set. Your task is to analyze the performance of k-NN algorithm in classifying photographs into...

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some specific node....

This paper, based on k-NN graph, presents symmetric (k, j)-NN graph (1 ≤ j < k), a brand new topology which could be adopted by a series of network-based structures. We show that the k nearest neighbors of a node exert disparate influence on guaranteeing network connectivity, and connections with the farthest j ones among these k neighbors are competent to build up a connected network, contrast...

We derive high-probability finite-sample uniform rates of consistency for k-NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that kNN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the k-NN regression rates to establish new results about estimating the level sets and global maxima of a function from noisy o...