The Experts below are selected from a list of 6519 Experts worldwide ranked by ideXlab platform
Tianfu Wu - One of the best experts on this subject based on the ideXlab platform.
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Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Tianfu WuAbstract:Many popular object detectors, such as AdaBoost, SVM and deformable part-based models (DPM), compute additive scoring Functions at a large number of windows in an image pyramid, thus computational efficiency is an important consideration in real time applications besides accuracy. In this paper, a decision policy refers to a sequence of two-sided thresholds to execute early reject and early accept based on the cumulative scores at each step. We formulate an empirical Risk Function as the weighted sum of the cost of computation and the loss of false alarm and missing detection. Then a policy is said to be cost-sensitive and optimal if it minimizes the Risk Function. While the Risk Function is complex due to high-order correlations among the two-sided thresholds, we find that its upper bound can be optimized by dynamic programming efficiently. We show that the upper bound is very tight empirically and thus the resulting policy is said to be near-optimal. In experiments, we show that the decision policy outperforms state-of-the-art cascade methods significantly, in several popular detection tasks and benchmarks, in terms of computational efficiency with similar accuracy of detection.
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Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection
2013 IEEE International Conference on Computer Vision, 2013Co-Authors: Tianfu WuAbstract:Many object detectors, such as AdaBoost, SVM and deformable part-based models (DPM), compute additive scoring Functions at a large number of windows scanned over image pyramid, thus computational efficiency is an important consideration beside accuracy performance. In this paper, we present a framework of learning cost-sensitive decision policy which is a sequence of two-sided thresholds to execute early rejection or early acceptance based on the accumulative scores at each step. A decision policy is said to be optimal if it minimizes an empirical global Risk Function that sums over the loss of false negatives (FN) and false positives (FP), and the cost of computation. While the Risk Function is very complex due to high-order connections among the two-sided thresholds, we find its upper bound can be optimized by dynamic programming (DP) efficiently and thus say the learned policy is near-optimal. Given the loss of FN and FP and the cost in three numbers, our method can produce a policy on-the-fly for Adaboost, SVM and DPM. In experiments, we show that our decision policy outperforms state-of-the-art cascade methods significantly in terms of speed with similar accuracy performance.
Martin L. Hazelton - One of the best experts on this subject based on the ideXlab platform.
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Symmetric adaptive smoothing regimens for estimation of the spatial relative Risk Function
Computational Statistics & Data Analysis, 2016Co-Authors: Tilman M. Davies, Khair Jones, Martin L. HazeltonAbstract:The spatial relative Risk Function is now regarded as a standard tool for visualising spatially tagged case-control data. This Function is usually estimated using the ratio of kernel density estimates. In many applications, spatially adaptive bandwidths are essential to handle the extensive inhomogeneity in the distribution of the data. Earlier methods have employed separate, asymmetrical smoothing regimens for case and control density estimates. However, we show that this can lead to potentially misleading methodological artefacts in the resulting estimates of the log-relative Risk Function. We develop a symmetric adaptive smoothing scheme that addresses this problem. We study the asymptotic properties of the new log-relative Risk estimator, and examine its finite sample performance through an extensive simulation study based on a number of problems adapted from real life applications. The results are encouraging.
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Generalizing the spatial relative Risk Function.
Spatial and Spatio-temporal Epidemiology, 2014Co-Authors: W.t.p. Sarojinie Fernando, Martin L. HazeltonAbstract:Abstract The spatial relative Risk Function is defined as the ratio of densities describing respectively the spatial distribution of cases and controls. It has proven to be an effective tool for visualizing spatial variation in Risk in many epidemiological applications over the past 20 years. We discuss the generalization of this Function to spatio-temporal case-control data, and also to situations where there are covariates available that may affect the spatial patterns of disease. We examine estimation of the generalized relative Risk Functions using kernel smoothing, including asymptotic theory and data-driven bandwidth selection. We also consider construction of tolerance contours. Our methods are illustrated on spatio-temporal data describing the 2001 outbreak of foot-and-mouth disease in the United Kingdom, with farm size as a covariate.
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A comparison of estimators of the geographical relative Risk Function
Journal of Statistical Computation and Simulation, 2012Co-Authors: W. T.p.s. Fernando, S. Ganesalingam, Martin L. HazeltonAbstract:The geographical relative Risk Function is a useful tool for investigating the spatial distribution of disease based on case and control data. The most common way of estimating this Function is using the ratio of bivariate kernel density estimates constructed from the locations of cases and controls, respectively. An alternative is to use a local-linear (LL) estimator of the log-relative Risk Function. In both cases, the choice of bandwidth is critical. In this article, we examine the relative performance of the two estimation techniques using a variety of data-driven bandwidth selection methods, including likelihood cross-validation (CV), least-squares CV, rule-of-thumb reference methods, and a new approximate plug-in (PI) bandwidth for the LL estimator. Our analysis includes the comparison of asymptotic results; a simulation study; and application of the estimators on two real data sets. Our findings suggest that the density ratio method implemented with the least-squares CV bandwidth selector is genera...
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sparr analyzing spatial relative Risk using fixed and adaptive kernel density estimation in r
Journal of Statistical Software, 2011Co-Authors: Tilman M. Davies, Martin L. Hazelton, Jonathan C MarshallAbstract:The estimation of kernel-smoothed relative Risk Functions is a useful approach to examining the spatial variation of disease Risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative Risk Function per se. Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative Risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated Risk Function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and Risk Functions, identify statistically significant fluctuations in an estimated Risk Function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.
Susan Jarvis - One of the best experts on this subject based on the ideXlab platform.
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marine mammal passive acoustics applied to the monitoring of long term trends in beaked whale abundance and to the derivation of a behavioral Risk Function for exposure to mid frequency active sonar
Journal of the Acoustical Society of America, 2016Co-Authors: David Moretti, Jessica Shaffer, Len Thomas, Tiago A. Marques, Stephanie L Watwood, Nancy Dimarzio, Karin Dolan, Ronald Morrissey, Joao F Monteiro, Susan JarvisAbstract:Knowledge of Cuvier’s (Ziphius cavirostris) and Blainville’s (Mesoplodon densirostris) beaked whales’ distinct dive and vocal behavior has allowed for the development of multiple methods of passive acoustic abundance and density estimation (Marques et al., 2009, Moretti et al., 2010). These methods are being applied to multiple years of data to estimate long-term trends in abundance for Blainville’s beaked whales at the Atlantic Undersea Test and Evaluation Center (AUTEC) in the Bahamas and at the Pacific Missile Range Facility (PMRF) in Hawaii, and for Cuvier’s beaked whales at the Southern California Offshore Range (SCORE). These passive acoustic beaked whale dive data were combined with sonar and Range ship-track data to derive a behavioral Risk Function for Blainville’s beaked whales at AUTEC, and are being extended to Cuvier’s beaked whales at SCORE and Blainville’s beaked whales at PMRF. The behavioral Risk Function maps the probability of beaked whale dive disruption as a Function of the receive le...
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A Risk Function for behavioral disruption of Blainville's beaked whales (Mesoplodon densirostris) from mid-frequency active sonar.
PLOS ONE, 2014Co-Authors: David Moretti, Ashley Dilley, Bert Neales, Jessica Shaffer, Elena Mccarthy, John Harwood, Len Thomas, Tiago A. Marques, Susan JarvisAbstract:There is increasing concern about the potential effects of noise pollution on marine life in the world’s oceans. For marine mammals, anthropogenic sounds may cause behavioral disruption, and this can be quantified using a Risk Function that relates sound exposure to a measured behavioral response. Beaked whales are a taxon of deep diving whales that may be particularly susceptible to naval sonar as the species has been associated with sonar-related mass stranding events. Here we derive the first empirical Risk Function for Blainville’s beaked whales (Mesoplodon densirostris) by combining in situ data from passive acoustic monitoring of animal vocalizations and navy sonar operations with precise ship tracks and sound field modeling. The hydrophone array at the Atlantic Undersea Test and Evaluation Center, Bahamas, was used to locate vocalizing groups of Blainville’s beaked whales and identify sonar transmissions before, during, and after Mid-Frequency Active (MFA) sonar operations. Sonar transmission times and source levels were combined with ship tracks using a sound propagation model to estimate the received level (RL) at each hydrophone. A generalized additive model was fitted to data to model the presence or absence of the start of foraging dives in 30-minute periods as a Function of the corresponding sonar RL at the hydrophone closest to the center of each group. This model was then used to construct a Risk Function that can be used to estimate the probability of a behavioral change (cessation of foraging) the individual members of a Blainville’s beaked whale population might experience as a Function of sonar RL. The Function predicts a 0.5 probability of disturbance at a RL of 150dBrms re µPa (CI: 144 to 155) This is 15dB lower than the level used historically by the US Navy in their Risk assessments but 10 dB higher than the current 140 dB step-Function.
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A Risk Function for behavioral disruption of Blainville's beaked whales (Mesoplodon densirostris) from Mid-Frequency Active sonar
PLoS ONE, 2014Co-Authors: David Moretti, Tiago Marques, Ashley Dilley, Bert Neales, Jessica Shaffer, Elena Mccarthy, Leslie New, John Harwood, Len Thomas, Susan JarvisAbstract:There is increasing concern about the potential effects of noise pollution on marine life in the world's oceans. For marine mammals, anthropogenic sounds may cause behavioral disruption, and this can be quantified using a Risk Function that relates sound exposure to a measured behavioral response. Beaked whales are a taxon of deep diving whales that may be particularly susceptible to naval sonar as the species has been associated with sonar-related mass stranding events. Here we derive the first empirical Risk Function for Blainville's beaked whales (Mesoplodon densirostris) by combining in situ data from passive acoustic monitoring of animal vocalizations and navy sonar operations with precise ship tracks and sound field modeling. The hydrophone array at the Atlantic Undersea Test and Evaluation Center, Bahamas, was used to locate vocalizing groups of Blainville's beaked whales and identify sonar transmissions before, during, and after Mid-Frequency Active (MFA) sonar operations. Sonar transmission times and source levels were combined with ship tracks using a sound propagation model to estimate the received level (RL) at each hydrophone. A generalized additive model was fitted to data to model the presence or absence of the start of foraging dives in 30-minute periods as a Function of the corresponding sonar RL at the hydrophone closest to the center of each group. This model was then used to construct a Risk Function that can be used to estimate the probability of a behavioral change (cessation of foraging) the individual members of a Blainville's beaked whale population might experience as a Function of sonar RL. The Function predicts a 0.5 probability of disturbance at a RL of 150dBrms re mPa (CI: 144 to 155) This is 15dB lower than the level used historically by the US Navy in their Risk assessments but 10 dB higher than the current 140 dB step-Function. © 201 4 Moretti et al.
Mariapaola Lanti - One of the best experts on this subject based on the ideXlab platform.
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comparison of the framingham Risk Function based coronary chart with Risk Function from an italian population study
European Heart Journal, 2000Co-Authors: Alessandro Menotti, Paolo Emilio Puddu, Mariapaola LantiAbstract:Methods and Results Coronary Risk Function in this study was the result of longitudinal experience in an Italian middle-aged population of 1656 male subjects followed-up for 25 years. To comply with the Framingham equation the same Risk factors (age, systolic blood pressure, total serum cholesterol and smoking habits), end-points (any possible coronary event including angina pectoris), and length of follow-up (10 years) were used, and the model (log-linear accelerated time failure model, accommodating the Weibull distribution) was similar. Comparisons were made computing the coronary Risk for each cell of the coronary Risk chart for men aged 40, 50 and 60 years. The Italian Risk Function produced highly significant coefficients for all four Risk factors. Forty-four out of a total of 120 cells had a coronary Risk of 20% or more in 10 years following the coronary Risk chart, whereas this was reduced to four while using the Italian Risk Function (P<0·001). The Italian Risk Function largely underestimated the corresponding levels produced by the coronary Risk chart and vice versa. Conclusion The Framingham Risk Function-based coronary Risk chart overestimates absolute coronary Risk in countries characterized by a lower incidence of coronary events and should be used with caution. (Eur Heart J 2000; 21: 365–370)
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Comparison of the Framingham Risk Function-based coronary chart with Risk Function from an Italian population study
European Heart Journal, 2000Co-Authors: Alessandro Menotti, Paolo Emilio Puddu, Mariapaola LantiAbstract:Methods and Results Coronary Risk Function in this study was the result of longitudinal experience in an Italian middle-aged population of 1656 male subjects followed-up for 25 years. To comply with the Framingham equation the same Risk factors (age, systolic blood pressure, total serum cholesterol and smoking habits), end-points (any possible coronary event including angina pectoris), and length of follow-up (10 years) were used, and the model (log-linear accelerated time failure model, accommodating the Weibull distribution) was similar. Comparisons were made computing the coronary Risk for each cell of the coronary Risk chart for men aged 40, 50 and 60 years. The Italian Risk Function produced highly significant coefficients for all four Risk factors. Forty-four out of a total of 120 cells had a coronary Risk of 20% or more in 10 years following the coronary Risk chart, whereas this was reduced to four while using the Italian Risk Function (P
Kyoungjae Kim - One of the best experts on this subject based on the ideXlab platform.
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financial time series forecasting using support vector machines
Neurocomputing, 2003Co-Authors: Kyoungjae KimAbstract:Abstract Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a Risk Function consisting of the empirical error and a regularized term which is derived from the structural Risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
