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2,015
Mean shift, mode seeking, and clustering
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeking proce ..."
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Cited by 624 (0 self)
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Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a mode
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
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Cited by 2395 (37 self)
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the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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in a more gen eral setting? We compare the marginals com puted using loopy propagation to the exact ones in four Bayesian network architectures, including two realworld networks: ALARM and QMR. We find that the loopy beliefs of ten converge and when they do, they give a good approximation
Integration of the cognitive and the psychodynamic unconscious
 American Psychologist
, 1994
"... Cognitiveexperiential selftheory integrates the cognitive and the psychodynamic unconscious by assuming the existence of two parallel, interacting modes of information processing: a rational system and an emotionally driven experiential system. Support for the theory is provided by the convergenc ..."
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Cited by 477 (1 self)
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by the convergence of a wide variety of theoretical positions on two similar processing modes; by reallife phenomena—such as conflicts between the heart and the head; the appeal of concrete, imagistic, and narrative representations; superstitious thinking; and the ubiquity of religion throughout recorded history
Refining Initial Points for KMeans Clustering
, 1998
"... Practical approaches to clustering use an iterative procedure (e.g. KMeans, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition fro ..."
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Cited by 317 (5 self)
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from a given initial one that is based on an efficient technique for estimating the modes of a distribution. The refined initial starting condition allows the iterative algorithm to converge to a "better" local minimum. The procedure is applicable to a wide class of clustering algorithms
The brain’s default network: Anatomy, function, and relevance to disease
 Annals of the New York Academy of Sciences
, 2008
"... Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, an ..."
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Cited by 316 (7 self)
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Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific
Observations of the Crab Nebula with the HEGRA system of IACTs in convergent mode using a topological trigger
"... Routine observations of the Crab Nebula for a total of about 250 h, performed with the HEGRA stereoscopic system of five imaging atmospheric Cerenkov telescopes in the standard operational mode, have proven the energy threshold of the system to be 500 GeV for small zenith angles (h6 20). A topologic ..."
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topological trigger applied along with the convergent observational mode allows to reduce noticeably the energy threshold of the system down to 350 GeV. Here we present the relevant Monte Carlo simulations as well as the analysis results of 15 h Crab Nebula data taken in such an observational mode. From
What Do Packet Dispersion Techniques Measure?
 IN PROCEEDINGS OF IEEE INFOCOM
, 2001
"... The packet pair technique estimates the capacity of a path (bottleneck bandwidth) from the dispersion (spacing) experienced by two backtoback packets [1][2][3]. We demonstrate that the dispersion of packet pairs in loaded paths follows a multimodal distribution, and discuss the queueing effects th ..."
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Cited by 313 (8 self)
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that cause the multiple modes. We show that the path capacity is often not the global mode, and so it cannot be estimated using standard statistical procedures. The effect of the size of the probing packets is also investigated, showing that the conventional wisdom of using maximum sized packet pairs
Treebased batch mode reinforcement learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state a ..."
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Cited by 224 (42 self)
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Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state
Results 1  10
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2,015