Results 11  20
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164
Filtering requirements for gradientbased optical flow measurement
 IEEE Trans. Image Proc. 2000
"... optical flow measurement ..."
A delay damage model selection algorithm for NARX neural networks
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 1997
"... Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although embedded memory ..."
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Cited by 9 (1 self)
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Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intelligent memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction.
An Experimental Study of Temperature Effect on Modal Parameters of the Alamosa Canyon Bridge
 of the Alamosa Canyon Bridge.” Earthquake Eng. and Structural Dynamics
, 1999
"... This paper examines a linear adaptive model to discriminate the changes of modal parameters due to temperature changes from those caused by structural damage or other environmental effects. Data from the Alamosa Canyon Bridge in the state of New Mexico were used to demonstrate the effectiveness of t ..."
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Cited by 7 (4 self)
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This paper examines a linear adaptive model to discriminate the changes of modal parameters due to temperature changes from those caused by structural damage or other environmental effects. Data from the Alamosa Canyon Bridge in the state of New Mexico were used to demonstrate the effectiveness of the adaptive filter for this problem. Results indicate that a linear fourinput (two time and two spatial dimensions) filter of temperature can reproduce the natural variability of the frequencies with respect to time of day. Using this simple model, we attempt to establish a confidence interval of the frequencies for a new temperature profile in order to discriminate the natural variation due to temperature
Making Correct Statistical Inferences using a Wrong Probability Model
, 1995
"... Large sample methods for estimating the variance of parameter estimates for hypothesistesting purposes (White, 1982) and statistical tests for model selection (Vuong, 1989) when the statistical model is wrong (i.e., misspecified) are reviewed. A parallel distributed processing (PDP) statistical mode ..."
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Cited by 7 (3 self)
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Large sample methods for estimating the variance of parameter estimates for hypothesistesting purposes (White, 1982) and statistical tests for model selection (Vuong, 1989) when the statistical model is wrong (i.e., misspecified) are reviewed. A parallel distributed processing (PDP) statistical model for analyzing categorical time series data is then proposed, and a theorem establishing when the quasimaximum likelihood estimates of the model are unique is stated and proved. Analyses of Golden et al.'s (1993) categorical timeseries data with respect to the proposed PDP model showed that White's asymptotic statistical theory yielded results which were more consistent with bootstrap estimates than classical methods of statistical inference. Making Correct Statistical Inferences 2 Ideally, a statistical analysis should be as "modelindependent" as possible making a minimal number of assumptions about the nature of the data generating process. Such an analysis is exemplary of the class...
Experimental
, 2005
"... ABSTRACT: The essential oils of the neotropical deciduous tree Bursera chemapodicta are shortchain alkanes and alkanoic derivatives, which contrast to the terpenoid resins of other characterized Bursera species. The resin composition of B. chemapodicta differs from leaf to twig, indicating a tissue ..."
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Cited by 6 (0 self)
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ABSTRACT: The essential oils of the neotropical deciduous tree Bursera chemapodicta are shortchain alkanes and alkanoic derivatives, which contrast to the terpenoid resins of other characterized Bursera species. The resin composition of B. chemapodicta differs from leaf to twig, indicating a tissue specificity for nonterpenoid essential oil synthesis. Fieldcollected leaves contained an average of 4.38 % heptane and 5.32 % total semivolatiles. This is a major switch in resin chemistry from terpenoid to linear alkanes within the genus Bursera. Copyright © 2006 John Wiley & Sons, Ltd. KEY WORDS: Bursera chemapodicta; essential oils chemistry; alkane oleoresins; Burseraceae
Inferring pH from diatoms: a comparison of old and new calibration methods. Hydrobiologia
, 1989
"... Two new methods for inferring pH from diatoms are presented. Both are based on the observation that the relationships between diatom taxa and pH are often unimodal. The first method is maximum likelihood calibration based on Gaussian logit response curves of taxa against pH. The second is weighted a ..."
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Cited by 6 (0 self)
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Two new methods for inferring pH from diatoms are presented. Both are based on the observation that the relationships between diatom taxa and pH are often unimodal. The first method is maximum likelihood calibration based on Gaussian logit response curves of taxa against pH. The second is weighted averaging. In a lake with a particular pH, taxa with an optimum close to the lake pH will be most abundant, so an intuitively reasonable estimate of the lake pH is to take a weighted average of the pH optima of the species present. Optima and tolerances of diatom taxa were estimated from contemporary pH and proportional diatom counts in littoral zone samples from 97 pristine soft water lakes and pools in Western Europe. The optima showed a strong relation with Hustedt’s pH preference groups. The two new methods were then compared with existing calibration methods on the basis of differences between inferred and observed pH in a test set of 62 additional samples taken between 1918 and 1983. The methods were ranked in order of performance as follows (between brackets the standard error of inferred pH in pH units); maximum likelihood (0.63)> weighted averaging (0.71) = multiple regression using pH groups (0.71) = the Gasse & Tekaia method (0.71)> Renberg & Hellberg’s Index B (0.83) % multiple regression
Interpreting canonical correlation analysis through biplots of structural correlations and weights
 Psychometrika
, 1990
"... This paper extends the biplot technique to canonical correlation analysis and redundancy analysis, The plot of structure correlations is shown to be optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression ..."
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Cited by 6 (1 self)
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This paper extends the biplot technique to canonical correlation analysis and redundancy analysis, The plot of structure correlations is shown to be optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression and canonical correlation analysis/redundancy analysis is exploited for producing an optimal biplot that displays a matrix of regression coefficients. This plot can be made from the canonical weights of the predictors and the structure correlations of the criterion variables. An example is used to show how the proposed biptots may be interpreted. Key words: biplot, canonical correlation analysis, canonical weight, interbattery factor analysis, partial analysis, redundancy analysis, regression coefficient, reduced rank regression, structure correlations.
Kernel Principal Component Regression with EM Approach to Nonlinear Principal Components Extraction
, 2000
"... In kernel based methods such as Support Vector Machines, Kernel PCA, Gaussian Processes or Regularization Networks the computational requirements scale as O(n 3 ) where n is the number of training points. In this paper we investigate Kernel Principal Component Regression (KPCR) with the Expect ..."
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Cited by 6 (2 self)
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In kernel based methods such as Support Vector Machines, Kernel PCA, Gaussian Processes or Regularization Networks the computational requirements scale as O(n 3 ) where n is the number of training points. In this paper we investigate Kernel Principal Component Regression (KPCR) with the Expectation Maximization approach in estimating of the subset of p principal components (p < n) in a feature space dened by a positive denite kernel function. The computational requirements of the method are O(pn²). Moreover, the algorithm can be implemented with memory requirements O(p²) +O((p + 1)n)). We give the theoretical description explaining how by the proper selection of a subset of nonlinear principal components desired generalization of the KPCR is achieved. On two data sets we experimentally demonstrate this fact. Moreover, on a noisy chaotic MackeyGlass time series prediction the best performance is achieved with p n and experiments also suggests that in such c...
Cosmography: Extracting the Hubble series from the supernova data
"... Cosmography (cosmokinetics) is the part of cosmology that proceeds by making minimal dynamic assumptions. One keeps the geometry and symmetries of FLRW spacetime, at least as a working hypothesis, but does not assume the Friedmann equations (Einstein equations), unless and until absolutely necessary ..."
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Cited by 5 (3 self)
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Cosmography (cosmokinetics) is the part of cosmology that proceeds by making minimal dynamic assumptions. One keeps the geometry and symmetries of FLRW spacetime, at least as a working hypothesis, but does not assume the Friedmann equations (Einstein equations), unless and until absolutely necessary. By doing so it is possible to defer questions about the equation of state of the cosmological fluid, and concentrate more directly on the observational situation. In particular, the “big picture ” is best brought into focus by performing a fit of all available supernova data to the Hubble relation, from the current epoch at least back to redshift z ≈ 1.75. We perform a number of interrelated cosmographic fits to the legacy05 and gold06 supernova datasets. We pay particular attention to the influence of both statistical and systematic uncertainties, and also to the extent to which the choice of distance scale and manner of representing the redshift scale affect the cosmological parameters. While the “preponderance of evidence ” certainly suggests an accelerating universe, we would argue that (based on the supernova data) this conclusion is not currently supported “beyond reasonable doubt”. As part of the analysis we develop two
Analyzing Resource Behavior Using Process Mining
 BPM 2009 Workshops, Proceedings of the Fifth Workshop on Business Process Intelligence (BPI’09), volume 43 of Lecture Notes in Business Information Processing
, 2010
"... Abstract. It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one examp ..."
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Cited by 5 (0 self)
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Abstract. It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one example is the speed at which people work. The “YerkesDodson Law of Arousal ” suggests that a worker that is under time pressure may become more efficient and thus finish tasks faster. However, if the pressure is too high, then the worker’s performance may degrade. Traditionally, it was difficult to investigate such phenomena and few analysis tools (e.g., simulation packages) support workloaddependent behavior. Fortunately, more and more activities are being recorded and modern process mining techniques provide detailed insights in the way that people really work. This paper uses a new process mining plugin that has been added to ProM to explore the effect of workload on service times. Based on historic data and by using regression analysis, the relationship between workload and services time is investigated. This information can be used for various types of analysis and decision making, including more realistic forms of simulation.