Results 11  20
of
107
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.
Filtering requirements for gradientbased optical flow measurement
 IEEE Trans. Image Proc. 2000
"... optical flow measurement ..."
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...
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...
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 6 (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
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
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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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
Biplots in reducedrank regression
 Biom. J
, 1994
"... SUMMARY Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reducedrank regression. Reducedrank regression combines multiple regression and pri ..."
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Cited by 5 (0 self)
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SUMMARY Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reducedrank regression. Reducedrank regression combines multiple regression and principal components analysis and can therefore be carried out with standard statistical packages. The proposed biplot highlights the major aspects of the regressions by displaying the leastsquares approximation of fitted values, regression coefficients and associated tratio's. The utility and interpretation of the reducedrank regression biplot is demonstrated with an example using public health data that were previously analyzed by separate multiple regressions.
Hierarchical forecasting of Web server workload using sequential Monte Carlo training
 In Proc. Conf. on Information Sciences and Systems
, 2006
"... Abstract—Internet service utilities host multiple server applications on a shared server cluster (server farm). One of the essential tasks of the hosting service provider is to allocate servers to each of the websites to maintain a certain level of quality of service for different classes of incomin ..."
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Cited by 5 (0 self)
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Abstract—Internet service utilities host multiple server applications on a shared server cluster (server farm). One of the essential tasks of the hosting service provider is to allocate servers to each of the websites to maintain a certain level of quality of service for different classes of incoming requests at each point of time, and optimize the use of server resources, while maximizing its profits. Such a proactive management of resources requires accurate prediction of workload, which is generally measured as the amount of service requests per unit time. As a time series, the workload exhibits not only short time random fluctuations but also prominent periodic (daily) patterns that evolve randomly from one period to another. We propose a solution to the Web server load prediction problem based on a hierarchical framework with multiple time scales. This framework leads to adaptive procedures that provide both longterm (in days) and shortterm (in minutes) predictions with simultaneous confidence bands which accommodate not only serial correlation but also heavytailedness, and nonstationarity of the data. The longterm load is modeled as a dynamic harmonic regression (DHR), the coefficients of which evolve according to a random walk, and are tracked using sequential Monte Carlo (SMC) algorithms; whereas the shortterm load is predicted using an autoregressive model, whose parameters are also estimated using SMC techniques. We evaluate our method using realworld Web workload data. Index Terms—Dynamic harmonic regression, seasonal time series, sequential Monte Carlo, Webload prediction. I.
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 5 (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.