By Jeff Erickson (auth.), Frank Dehne, Jörg-Rüdiger Sack, Norbert Zeh (eds.)
The papers during this quantity have been awarded on the tenth Workshop on Algorithms and knowledge constructions (WADS 2005). The workshop came about August 15 - 17, 2007, at Dalhousie collage, Halifax, Canada. The workshop alternates with the Scandinavian Workshop on set of rules thought (SWAT), carrying on with the t- dition of SWAT and WADS beginning with SWAT 1988 and WADS 1989. From 142 submissions, this system Committee chosen fifty four papers for presentation on the workshop. furthermore, invited lectures got by way of the next dist- guished researchers: Je? Erickson (University of Illinois at Urbana-Champaign) and Mike Langston (University of Tennessee). On behalf of this system Committee, we wish to precise our honest appreciation to the numerous people whose e?ort contributed to creating WADS 2007 successful. those comprise the invited audio system, contributors of the steerage and ProgramCommittees, the authorswho submitted papers, andthe manyreferees who assisted this system Committee. we're indebted to Gerardo Reynaga for fitting and editing the submission software program, conserving the submission server and interacting with authors in addition to for aiding with the training of the program.
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Terasawa and Y. Tanaka and orthoplex method partition the space into d + 1 or 2d fragments. It is a milder partitioning than that of the partitioning into 2d fragments, but points far away will collide with very little probability. The aforementioned supports the eﬃcacy of our algorithm. 6 Conclusion In this paper we have proposed an algorithm to solve the approximate nearest neighbor problem when all points are constrained to lie on the surface of the unit hypersphere. Our algorithm, named SLSH, is based on the LSH scheme, and outperforms state-of-the-art LSH variants.
WADS 2007, LNCS 4619, pp. 27–38, 2007. c Springer-Verlag Berlin Heidelberg 2007 28 K. Terasawa and Y. Tanaka In recent years, approximation methods have been proposed to overcome the curse of dimensionality. The c-approximate nearest neighbor problem is the relaxed problem that allows an output point to be at most c times distant than the exact nearest neighbor is. A randomized (or probabilistic) algorithm is also often employed to overcome the curse of dimensionality. With a ﬁxed parameter δ, the randomized algorithm should return requested point with a probability of no less than 1 − δ.
Vj of the node v. We say that a node v is on level l if the path from v to a leaf node consists of l edges. In every node v on level l > 1 the data structure Sv consists of two parts: array Av with log1/2 n entries and a word Bv with log1/2 n components of log log n bits. Following the idea of , we store the information about log1/2 n most recent updates in the word Bv and the information about the older updates Orthogonal Range Searching in Linear and Almost-Linear Space 21 in array Av .