dimensional Euclidean space. Given N points {xj} in Rd, the algorithm attempts to find k nearest neighbors for each of xj , where k is a user-specified integer parameter. The algorithm is iterative, and its CPU time requirements are proportional to T ·N ·(d ·(log d)+ k · (d + log k) · (log N)) + N · k2 · (d + log k), with T the number of iterations performed. The memory requirements of the proc...

As the healthcare industry becomes more reliant upon electronic records, the amount of medical data available for analysis increases exponentially. While this information contains valuable statistics, the sheer volume makes it difficult to analyze without efficient algorithms. By using machine learning to classify medical data, diagnoses can become more efficient, accurate, and accessible for t...

Distributional similarity is a useful notion in estimating the probabilities of rare joint events. It has been employed both to cluster events according to their distributions, and to directly compute averages of estimates for distributional neighbors of a target event. Here, we examine the tradeoffs between model size and prediction accuracy for cluster-based and nearest neighbors distribution...

Given an initial rectangular range or k nearest neighbor (k-nn) query (using the L1 metric), we consider the problems of incrementally extending the query by increasing the size of the range, or by increasing k, and reporting the new points incorporated by each extension. Although both problems may be solved trivially by repeatedly applying a traditional range query or L1 k-nn algorithm, such s...

dimensional Euclidean space. Given N points {xj} in Rd, the algorithm attempts to find k nearest neighbors for each of xj , where k is a user-specified integer parameter. The algorithm is iterative, and its CPU time requirements are proportional to T ·N ·(d ·(log d)+ k · (log k) · (log N)) + N · k2 · (d + log k), with T the number of iterations performed. The memory requirements of the procedur...

1053-5888/08/$20.00©2008IEEE IEEE SIGNAL PROCESSING MAGAZINE [128] MARCH 2008 T he Internet has brought us a wealth of data, all now available at our fingertips. We can easily carry in our pockets thousands of songs, hundreds of thousands of images, and hundreds of hours of video. But even with the rapid growth of computer performance, we don’t have the processing power to search this amount of...

The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training an...

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that...

While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile device...