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Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the World Wide Web
 IN PROC. 29TH ACM SYMPOSIUM ON THEORY OF COMPUTING (STOC
, 1997
"... We describe a family of caching protocols for distributed networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and ..."
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Cited by 699 (10 self)
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of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes minimally as the range of the function changes. Through the development of good
Using max cut to enhance rooted trees consistency
 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (TCBB
, 2006
"... Supertree methods are used to construct a large tree over a large set of taxa from a set of small trees over overlapping subsets of the complete taxa set. Since accurate reconstruction methods are currently limited to a maximum of a few dozen taxa, the use of a supertree method in order to construc ..."
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Cited by 21 (2 self)
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Supertree methods are used to construct a large tree over a large set of taxa from a set of small trees over overlapping subsets of the complete taxa set. Since accurate reconstruction methods are currently limited to a maximum of a few dozen taxa, the use of a supertree method in order
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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. The core of this method is a simple hillclimbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distancebased method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 484 (8 self)
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with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. tree
Tree Consistency and Bounds on the Performance of the MaxProduct Algorithm and Its Generalizations
, 2002
"... Finding the maximum a posteriori (MAP) assignment of a discretestate distribution specified by a graphical model requires solving an integer program. The maxproduct algorithm, also known as the maxplus or minsum algorithm, is an iterative method for (approximately) solving such a problem on gr ..."
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Cited by 65 (5 self)
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Finding the maximum a posteriori (MAP) assignment of a discretestate distribution specified by a graphical model requires solving an integer program. The maxproduct algorithm, also known as the maxplus or minsum algorithm, is an iterative method for (approximately) solving such a problem on graphs with cycles.
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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(RBF) kernels. Both the SVM and LSSVM classifier with RBF kernel in combination with standard crossvalidation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LSSVM performances are consistently very good when compared to a variety of methods described
A Scalable, Commodity Data Center Network Architecture
, 2008
"... Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfo ..."
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Cited by 466 (18 self)
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Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 455 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
Trees in trees: is the incomplete information about a tree consistent? ∗
"... We are interested in the following problem: given a tree automaton A and an incomplete tree description P, does a tree T exist such that T is accepted by A and consistent with P? A tree description is a treelike structure which provides incomplete information about the shape of T. We show that this ..."
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Cited by 1 (0 self)
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We are interested in the following problem: given a tree automaton A and an incomplete tree description P, does a tree T exist such that T is accepted by A and consistent with P? A tree description is a treelike structure which provides incomplete information about the shape of T. We show
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