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Nearoptimal hashing algorithms for approximate nearest neighbor in high dimensions
, 2008
"... In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The ..."
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Cited by 457 (7 self)
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In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query
NearOptimal Hashing Algorithms for Approximate Nearest Neighbor in HighDimensions*
"... Abstract We present an algorithm for the capproximate nearest neighbor problem in a ddimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashingbased algorithm recently obtained in [27]. We alsoobtain a spac ..."
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Abstract We present an algorithm for the capproximate nearest neighbor problem in a ddimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashingbased algorithm recently obtained in [27]. We alsoobtain a
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 521 (8 self)
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inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate
Nearoptimal sensor placements in gaussian processes
 In ICML
, 2005
"... When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in t ..."
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Cited by 342 (34 self)
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) in the GP model, and A, D, or Eoptimal design. In this paper, we tackle the combinatorial optimization problem of maximizing the mutual information between the chosen locations and the locations which are not selected. We prove that the problem of finding the configuration that maximizes mutual
Efficient Variants of the ICP Algorithm
 INTERNATIONAL CONFERENCE ON 3D DIGITAL IMAGING AND MODELING
, 2001
"... The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of threedimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minim ..."
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Cited by 718 (5 self)
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The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of threedimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points
A Fast and Elitist MultiObjective Genetic Algorithm: NSGAII
, 2000
"... Multiobjective evolutionary algorithms which use nondominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) nonelitism approach, and (iii) the need for specifying a sharing param ..."
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Cited by 1815 (60 self)
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, is able to find much better spread of solutions and better convergence near the true Paretooptimal front compared to PAES and SPEA  two other elitist multiobjective EAs which pay special attention towards creating a diverse Paretooptimal front. Moreover, we modify the definition of dominance in order
Nearoptimal reinforcement learning in polynomial time
 Machine Learning
, 1998
"... We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve nearoptimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the m ..."
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Cited by 304 (5 self)
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We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve nearoptimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded
NearOptimal Space Perfect Hashing Algorithms
"... Abstract. A perfect hash function (PHF) is an injective function that maps keys from a set S to unique values. Since no collisions occur, each key can be retrieved from a hash table with a single probe. A minimal perfect hash function (MPHF) is a PHF with the smallest possible range, that is, the ha ..."
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Cited by 4 (0 self)
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bound to describe a minimal perfect hash function was approximately 1.44 bits per key. Although the proof indicates that it would be possible to build an algorithm capable of generating optimal functions, no one was able to obtain a practical algorithm that could be used in real applications. Thus
Splitters and nearoptimal derandomization
"... We present a fairly general method for finding deterministic constructions obeying what we call krestrictions; this yields structures of size not much larger than the probabilistic bound. The structures constructed by our method include (n; k)universal sets (a collection of binary vectors of lengt ..."
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Cited by 60 (1 self)
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of length n such that for any subset of size k of the indices, all 2k configurations appear) and families of perfect hash functions. The nearoptimal constructions of these objects imply the very efficient derandomization of algorithms in learning, of fixedsubgraph finding algorithms, and of near optimal
Software pipelining: An effective scheduling technique for VLIW machines
, 1988
"... This paper shows that software pipelining is an effective and viable scheduling technique for VLIW processors. In software pipelining, iterations of a loop in the source program are continuously initiated at constant intervals, before the preceding iterations complete. The advantage of software pipe ..."
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Cited by 581 (3 self)
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pipelining is that optimal performance can be achieved with compact object code. This paper extends previous results of software pipelining in two ways: First, this paper shows that by using an improved algorithm, nearoptimal performance can be obtained without specialized hardware. Second, we propose a
Results 1  10
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