M. Murphy and S. S. Skiena. Ranger: A tool for nearest neighbor search in high dimensions. In Proc. 9th Annu. ACM Sympos. Comput. Geom., pages 403--404, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... system GASP [41] We measured the approximations of arrangements and also applied these approximations to the problem of computing discrepancies [39] Approximations To test the quality of our algorithm, we created approximations for line arrangements generated by different random distributions [33], and created their dual lines according to the duality transform Dual2. This set of lines was the input to our algorithm. To illustrate the quality our results, each of the Figures 2 6 presents the arrangement of a set of 50 lines generated with a specific distribution, and the approximating ....

M. Murphy and S. S. Skiena. Ranger: A tool for nearest neighbor search in high dimensions. In Proc. 9th Annu. ACM Sympos. Comput. Geom., pages 403--404, 1993.

....object. If the element e that has been removed from the queue is a data object, then e is reported as the next nearest neighbor of the query object. In order to be able to visualize the behavior of the incremental nearest neighbor algorithm, at any instance of time (see Lenhof Smid (1994) and Murphy Skiena (1993) for alternative nearest neighbor algorithm animations) we distinguishbetween the following entities by using different colors. 1. Bounding boxes in the priority queue are denoted by light blue. 2. Objects in the priority queue are denoted by green. 3. Objects that have not yet been processed ....

Murphy, M. & Skiena, S. S. (1993), Ranger: A tool for nearest neighbor search high dimensions, in `Proceedings of the Ninth Annual Symposium on Computational Geometry', San Diego, CA, pp. 403--404.

....for which it received the highest rating, as well its average rating across all problems. LEDA [2] received almost as many hits (2084) as the two following implementations, both associated with popular books [4] 1258) and [1] 994) The fourth most popular implementation was (surprisingly) Ranger [3] (846) an implementation of kd trees. This reflects the enormous popularity of nearest neighbor searching in higher dimensions, as well as the fact that I have not updated the list of implementations since the publication of the book in November. Arya and Mount s recently released ANN ....

M. Murphy and S. Skiena. Ranger: A tool for nearest neighbor search in high dimensions. In Proc. Ninth ACM Symposium on Computational Geometry, pages 403--404, 1993.

....which it received the highest rating, as well its average rating across all problems. LEDA [3] received almost as many hits (8806) as the two following implementations, both associated with popular books [5] 5339) and [2] 4360) The fourth most popular implementation was (surprisingly) Ranger [4] (3514) an implementation of kd trees. This reflects the enormous popularity of nearest neighbor searching in higher dimensions, as well as the fact that I have not updated the list of implementations since the publication of the book in November 1997. Arya and Mount s recently released ANN ....

M. Murphy and S. Skiena. Ranger: A tool for nearest neighbor search in high dimensions. In Proc. Ninth ACM Symposium on Computational Geometry, pages 403--404, 1993.

No context found.

Michael Murphy and Steven S. Skiena "Ranger: A Tool for Nearest Neighbor Search in High Dimensions"

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC