The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Jason Goetz - One of the best experts on this subject based on the ideXlab platform.
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evaluating machine learning and Statistical Prediction techniques for landslide susceptibility modeling
Computers & Geosciences, 2015Co-Authors: Jason Goetz, Alexander Brenning, Helene Petschko, Philip LeopoldAbstract:Abstract Statistical and now machine learning Prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional Statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k -fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the Prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan curvature were consistently highly ranked variables. The Prediction methods that create splits in the predictors (RF, BPLDA and WOE) resulted in heterogeneous Prediction maps full of spatial artifacts. In contrast, the GAM, GLM and SVM produced smooth Prediction surfaces. Overall, it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeling technique.
Michael Elad - One of the best experts on this subject based on the ideXlab platform.
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a Statistical Prediction model based on sparse representations for single image super resolution
IEEE Transactions on Image Processing, 2014Co-Authors: Tomer Peleg, Michael EladAbstract:We address single image super-resolution using a Statistical Prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
Basil Papadopoulos - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy-Statistical Prediction intervals from crisp regression models
Evolving Systems, 2019Co-Authors: Kingsley Adjenughwure, Basil PapadopoulosAbstract:Most Prediction outputs from regression models are either point estimates or interval estimates. Point estimates from a model are useful for making conclusions about model accuracy. Interval estimates on the other-hand are used to evaluate the uncertainty in the model Predictions. These two approaches only produce either point or a single interval and thus do not fully represent the uncertainties in the model Prediction. In this paper, previous works on constructing fuzzy numbers from arbitrary Statistical intervals are extended by first constructing fuzzy-Statistical Prediction intervals which combines point and Prediction interval estimates into a single fuzzy number which fully represents the uncertainties in the model. Then two simple metrics are introduced that can evaluate the quality of the proposed fuzzy-Statistical Prediction intervals. The proposed metrics are simple to calculate and use same ideas from the well-known metrics for evaluating interval estimates. To test the applicability of the proposed method, two types of scenarios are adopted. In the first scenario, the models are calibrated and then the proposed method is used to get the fuzzy-Statistical Prediction interval. In the second scenario, the point estimate and Prediction intervals are given as output from a model by another researcher, then the proposed approach is used to get the fuzzy-Statistical Prediction intervals without prior knowledge of the model calibration process. The first scenario is tested by calibrating linear regression and neural network models using a well-known data set of automobile fuel consumption (auto-MPG). The second scenario is tested using outputs from point and interval estimates of two time series models (ARIMA, Kalman Filter) calibrated from a real traffic flow data set.
Karl Iagnemma - One of the best experts on this subject based on the ideXlab platform.
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IROS - A stochastic response surface approach to Statistical Prediction of mobile robot mobility
2008 IEEE RSJ International Conference on Intelligent Robots and Systems, 2008Co-Authors: Gaurav Kewlani, Karl IagnemmaAbstract:The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility Prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility Prediction algorithm. Here a method for efficient mobility Prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate Prediction of mobile robot mobility.
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a stochastic response surface approach to Statistical Prediction of mobile robot mobility
Intelligent Robots and Systems, 2008Co-Authors: Gaurav Kewlani, Karl IagnemmaAbstract:The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility Prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility Prediction algorithm. Here a method for efficient mobility Prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate Prediction of mobile robot mobility.
Tomer Peleg - One of the best experts on this subject based on the ideXlab platform.
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a Statistical Prediction model based on sparse representations for single image super resolution
IEEE Transactions on Image Processing, 2014Co-Authors: Tomer Peleg, Michael EladAbstract:We address single image super-resolution using a Statistical Prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
