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1,941,204
Solving Infinite Stochastic Process Algebra Models through MatrixGeometric Methods
"... We introduce a Stochastic Process Algebra called PEPA ph , based on Hillston's PEPA. PEPA ph is suitable for describing and analysing the performance of certain kinds of queues, such as Ph=Ph=c and M=Ph=1. ..."
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Cited by 6 (0 self)
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We introduce a Stochastic Process Algebra called PEPA ph , based on Hillston's PEPA. PEPA ph is suitable for describing and analysing the performance of certain kinds of queues, such as Ph=Ph=c and M=Ph=1.
ProductForm Approximation of Tandem Queues via Matrix Geometric Methods
"... Abstractâ€”We introduce a productform approximation for tandem networks with Poisson arrivals and nonexponential service times. The proposed technique perturbs the model state space to match the sufficient conditions for productform solution provided by the Reversed Compound Agent Theorem (RCAT). A ..."
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). After characterizing the relationship between RCAT productforms and matrix geometric solutions, we develop an algorithm based on nonlinear programming that automatically searches for an approximating productform model. I.
Perturbation Analysis Of The M/M/1 Queue In A Markovian Environment Via The MatrixGeometric Method
, 1993
"... In this paper, we consider a family of M(t)=M=1 queues in which customers arrive according to nonhomogenous Poisson processes with intensity t () = t , 0 < < 1. We assume that t () is an irreducible nitestate Markov process. Based on the matrixgeometric method, we use perturbation an ..."
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Cited by 2 (0 self)
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In this paper, we consider a family of M(t)=M=1 queues in which customers arrive according to nonhomogenous Poisson processes with intensity t () = t , 0 < < 1. We assume that t () is an irreducible nitestate Markov process. Based on the matrixgeometric method, we use perturbation
Steadystate analysis of infinite stochastic Petri nets: Comparing the spectral expansion and the matrixgeometric method
 Proc. 7th International Workshop on Petri Nets and Performance Models
, 1997
"... In this paper we investigate the e#ciency of two solution approaches to infinite stochastic Petri nets: the matrixgeometric method and the spectral expansion method. We first informally present infinite stochastic Petri nets, after which we describe, using uniform notation, the matrixgeometric and ..."
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Cited by 14 (5 self)
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In this paper we investigate the e#ciency of two solution approaches to infinite stochastic Petri nets: the matrixgeometric method and the spectral expansion method. We first informally present infinite stochastic Petri nets, after which we describe, using uniform notation, the matrixgeometric
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 780 (22 self)
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is contained in the socalled kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input spaceclassical model selection
Image registration methods: a survey
 IMAGE AND VISION COMPUTING
, 2003
"... This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align t ..."
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Cited by 734 (9 self)
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This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
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Cited by 1230 (5 self)
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Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
ANALYSIS OF IEEE 802.15.4 WITH NONBEACON ENABLED CSMA/CA BY MATRIX GEOMETRIC METHOD
"... Recently, there has been a significant increase in research of wireless sensor networks (WSN). Network communication requirement of WSN is different from that of the traditional network because the traditional performance criteria of network are throughput, latency and fairness, whereas in WSN, ener ..."
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Recently, there has been a significant increase in research of wireless sensor networks (WSN). Network communication requirement of WSN is different from that of the traditional network because the traditional performance criteria of network are throughput, latency and fairness, whereas in WSN, energy efficiency become more important. Making a system energy efficient in WSN is a challenging research topic and researchers have developed many algorithms [1,2,3]. In this paper, we propose an analytical model of IEEE 802.15.4 which is standardized toward low complexity, low power consumption and low data rate wireless data connectivity. This standard allows two network topologies: star and peertopeer. In a star topology, every sensors must communicate through PAN coordinator. In a peertopeer topology, all devices can communicate each other if both devices are within a physical range. In a star topology, network uses two types of network channel access mechanism. One is based on the slotted CSMA/CA in which slots are aligned with the beacon enabled. Another access mechanism is based on the unslotted CSMA/CA without beacon frame. This paper concentrates on the MAC performance of the IEEE 802.15.4 network with star
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
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Cited by 560 (15 self)
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algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely
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