Given a set of n points in d-dimensional Euclidean space, S E d , and a query point q 2 E d , we wish to determine the nearest neighbor of q, that is, the point of S whose Euclidean distance to q is minimum. The goal is to preprocess the point set S, such that queries can be answered as eeciently as possible. We assume that the dimension d is a constant independent of n. Although reasonably goo...

In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical classification problem, we use it for learning a distance function and the resulting distance is used into K-NN. The proposed method (Boosted Distance with Nearest Neighbor) outperforms the AdaBoost classifier when the tr...

The realization that entanglement can affect macroscopic properties of bulk solid-state systems is a challenge in physics and Chemistry. Theoretical physicists often are considered the entanglement between nearest-neighbor (NN) spins and tried to find its characterizations in terms of macroscopic thermodynamics observables as magnetization and specific heat. Here, we focus on the entanglement b...

Nearest neighbor-based techniques provide an approach to acoustic modeling that avoids the often lengthy and heuristic process of training traditional Gaussian mixturebased models. Here we study the problem of choosing the distance metric for a k-nearest neighbor (k-NN) phonetic frame classifier. We compare the standard Euclidean distance to two learned Mahalanobis distances, based on large-mar...

Constrained k nearest neighbor query for uncertain object in the network is to find k uncertain objects which are the k nearest neighbors with range constraint of the query object in the network. For solving this problem, the uncertain object is modeled as the fuzzy object and the network  -distance between fuzzy objects in the network is defined. Base on them, the concept of constrained k nea...