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943,209
The Cricket LocationSupport System
, 2000
"... This paper presents the design, implementation, and evaluation of Cricket, a locationsupport system for inbuilding, mobile, locationdependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze informatio ..."
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Cited by 1036 (11 self)
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This paper presents the design, implementation, and evaluation of Cricket, a locationsupport system for inbuilding, mobile, locationdependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze
New Support Vector Algorithms
, 2000
"... this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases ..."
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Cited by 461 (42 self)
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this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases
A fast iterative shrinkagethresholding algorithm with application to . . .
, 2009
"... We consider the class of Iterative ShrinkageThresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast Iterat ..."
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Cited by 1055 (8 self)
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We consider the class of Iterative ShrinkageThresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
"... ..."
Fast Algorithms for Mining Association Rules
, 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
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Cited by 3551 (15 self)
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algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scaleup experiments show that AprioriHybrid scales linearly
Orthonormal bases of compactly supported wavelets
, 1993
"... Several variations are given on the construction of orthonormal bases of wavelets with compact support. They have, respectively, more symmetry, more regularity, or more vanishing moments for the scaling function than the examples constructed in Daubechies [Comm. Pure Appl. Math., 41 (1988), pp. 90 ..."
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Cited by 2182 (27 self)
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Several variations are given on the construction of orthonormal bases of wavelets with compact support. They have, respectively, more symmetry, more regularity, or more vanishing moments for the scaling function than the examples constructed in Daubechies [Comm. Pure Appl. Math., 41 (1988), pp
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
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Cited by 887 (4 self)
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This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try
Language Support for Lightweight Transactions
, 2003
"... Concurrent programming is notoriously di#cult. Current abstractions are intricate and make it hard to design computer systems that are reliable and scalable. We argue that these problems can be addressed by moving to a declarative style of concurrency control in which programmers directly indicate t ..."
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Cited by 479 (16 self)
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Concurrent programming is notoriously di#cult. Current abstractions are intricate and make it hard to design computer systems that are reliable and scalable. We argue that these problems can be addressed by moving to a declarative style of concurrency control in which programmers directly indicate the safety properties that they require.
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 620 (1 self)
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large
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