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NeXt generation/dynamic spectrum access/cognitive Radio Wireless Networks: A Survey
 COMPUTER NETWORKS JOURNAL (ELSEVIER
, 2006
"... Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited availabl ..."
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Cited by 708 (16 self)
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Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited
SplitStream: HighBandwidth Multicast in Cooperative Environments
 SOSP '03
, 2003
"... In treebased multicast systems, a relatively small number of interior nodes carry the load of forwarding multicast messages. This works well when the interior nodes are highly available, d d cated infrastructure routers but it poses a problem for applicationlevel multicast in peertopeer systems. ..."
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Cited by 570 (17 self)
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In treebased multicast systems, a relatively small number of interior nodes carry the load of forwarding multicast messages. This works well when the interior nodes are highly available, d d cated infrastructure routers but it poses a problem for applicationlevel multicast in peertopeer systems
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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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 propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
SelfSimilarity Through HighVariability: Statistical Analysis of Ethernet LAN Traffic at the Source Level
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1997
"... A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is selfsimilar or longrange dependent in nature (i.e., bursty over a wide range of time scales)  in sharp contrast to commonly made tr ..."
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Cited by 740 (24 self)
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traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of selfsimilarity in LAN traffic. Our explanation is based on new convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical
Parallel database systems: the future of high performance database systems
 Communications of the ACM
, 1992
"... Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional sharednothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper ..."
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Cited by 638 (13 self)
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Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional sharednothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems. 1.
Recovering High Dynamic Range Radiance Maps from Photographs
"... We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the respon ..."
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Cited by 856 (15 self)
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We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover
Coda: A Highly Available File System for a Distributed Workstation Environment
 In IEEE Transactions on Computers
, 1990
"... Abstract Coda is a file system for a largescale distributed computing environment composed of Unix workstations. It provides resiliency to server and network failures through the use of two distinct but complementary mechanisms. One mechanism, server replication,stores copies of a file at multiple ..."
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Cited by 530 (46 self)
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, and strives to provide the highest degree of consistency attainable in the light of these objectives. Measurements from a prototype show that the performance cost of providing high availability in Coda is reasonable. Index Terms Andrew, availability, caching, disconnected operation, distributed file system
High confidence visual recognition of persons by a test of statistical independence
 IEEE Trans. on Pattern Analysis and Machine Intelligence
, 1993
"... Abstruct A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a samp ..."
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Cited by 596 (8 self)
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Abstruct A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the human population reveals variation corresponding to several hundred independent degreesoffreedom. Morphogenetic randomness in the texture expressed phenotypically in the iris trabecular meshwork ensures that a test of statistical independence on two coded patterns originating from different eyes is passed almost certainly, whereas the same test is failed almost certainly when the compared codes originate from the same eye. The visible texture of a person’s iris in a realtime video image is encoded into a compact sequence of multiscale quadrature 2D Gabor wavelet coefficients, whose mostsignificant bits comprise a 256byte “iris code. ” Statistical decision theory generates identification decisions from ExclusiveOR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical “crossover ” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about lo”’. Index Terms Image analysis, statistical pattern recognition, biometric identification, statistical decision theory, 2D Gabor filters, wavelets, texture analysis, morphogenesis. I.
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
 MACHINE LEARNING
, 1999
"... Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several variants in co ..."
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Cited by 695 (2 self)
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in conjunction with a decision tree inducer (three variants) and a NaiveBayes inducer.
The purpose of the study is to improve our understanding of why and
when these algorithms, which use perturbation, reweighting, and
combination techniques, affect classification error. We provide a
bias and variance
Using Daily Stock Returns: The Case of Event Studies
 Journal of Financial Economics
, 1985
"... This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies. Daily data generally present few difficulties for event studies. Standard procedures are typically wellspecified even when special daily data characteristics ..."
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Cited by 763 (2 self)
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tics are ignored. However, recognition of autocorrelation in daily excess returns and changes in their variance conditional on an event can sometimes be advantageous. In addition, tests ignoring crosssectional dependence can be wellspecified and have higher power than tests which account for potential dependence
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