Probabilistic neural networks and general regression neural networks
Fuzzy logic and neural network handbook
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Evaluation of adaptive neural network models for freeway incident detection
IEEE Transactions on Intelligent Transportation Systems
Traffic-incident detection-algorithm based on nonparametric regression
IEEE Transactions on Intelligent Transportation Systems
Real-time hazardous traffic condition warning system: framework and evaluation
IEEE Transactions on Intelligent Transportation Systems
A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a neural network model based on reduced multivariate polynomial pattern classifier for application in freeway incident detection. The reduced multivariate model (RM) is a recently proposed classifier model which is easy to implement and analyze, and has been observed to efficiently capture the nonlinear input-output relationships in many classification applications. Since the freeway incident detection can be treated as a two-category pattern classification problem, the reduced multivariate polynomial model is particularly suitable for this incident detection application. Both Recursive Singular Value Decomposition (RSVD)- based and gradient descent-based least square estimators were adopted to learn the RM classifier in this work. The comparison of results obtained with those from several other classification strategies demonstrates the efficacy of the proposed model for traffic incident detection.