The Experts below are selected from a list of 39417 Experts worldwide ranked by ideXlab platform
Edward Herskovits - One of the best experts on this subject based on the ideXlab platform.
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a Bayesian Method for constructing Bayesian belief networks from databases
arXiv: Artificial Intelligence, 2013Co-Authors: Gregory F Cooper, Edward HerskovitsAbstract:This paper presents a Bayesian Method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the Methods in this paper to previous work, and we discuss open problems.
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A Bayesian Method for the Induction of Probabilistic Networks from Data
Machine Learning, 1992Co-Authors: Gregory F Cooper, Edward HerskovitsAbstract:This paper presents a Bayesian Method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic Method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the Methods in this paper to previous work, and we discuss open problems.
Keiichi Tokuda - One of the best experts on this subject based on the ideXlab platform.
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Image recognition based on hidden Markov eigen-image models using variational Bayesian Method
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013Co-Authors: Kei Sawada, Kei Hashimoto, Yoshihiko Nankaku, Keiichi TokudaAbstract:An image recognition Method based on hidden Markov eigen-image models (HMEMs) using the variational Bayesian Method is proposed and experimentally evaluated. HMEMs have been proposed as a model with two advantageous properties: linear feature extraction based on statistical analysis and size-and-location-invariant image recognition. In many image recognition tasks, it is difficult to use sufficient training data, and complex models such as HMEMs suffer from the over-fitting problem. This study aims to accurately estimate HMEMs using the Bayesian criterion, which attains high generalization ability by using prior information and marginalization of model parameters. Face recognition experiments showed that the proposed Method improves recognition performance.
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Face recognition based on separable lattice 2-D HMMS using variational Bayesian Method
2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012Co-Authors: Kei Sawada, Akira Tamamori, Kei Hashimoto, Yoshihiko Nankaku, Keiichi TokudaAbstract:This paper proposes an image recognition technique based on separable lattice 2-D HMMs (SL2D-HMMs) using the variational Bayesian Method. SL2D-HMMs have been proposed to reduce the effect of geometric variations, e.g., size and location. The maximum likelihood criterion had previously been used in training SL2D-HMMs. However, in many image recognition tasks, it is difficult to use sufficient training data, and it suffers from the over-fitting problem. A higher generalization ability based on model marginalization is expected by applying the Bayesian criterion and useful prior information on model parameters can be utilized as prior distributions. Experiments on face recognition indicated that the proposed Method improved image recognition.
Thomas Rodet - One of the best experts on this subject based on the ideXlab platform.
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A subspace-based variational Bayesian Method
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yuling Zheng, Aurélia Fraysse, Thomas RodetAbstract:This paper is devoted to an improved variational Bayesian Method. Actually, variational Bayesian issue can be seen as a convex functional optimization problem. Our main contribution is the adaptation of subspace optimization Methods into the functional space involved in this problem. We highlight the efficiency of our Methodology on a linear inverse problem with a sparse prior. Comparisons with classical Bayesian Methods through a numerical example show the notable improved computation time.
Gregory F Cooper - One of the best experts on this subject based on the ideXlab platform.
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a Bayesian Method for constructing Bayesian belief networks from databases
arXiv: Artificial Intelligence, 2013Co-Authors: Gregory F Cooper, Edward HerskovitsAbstract:This paper presents a Bayesian Method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the Methods in this paper to previous work, and we discuss open problems.
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A Bayesian Method for the Induction of Probabilistic Networks from Data
Machine Learning, 1992Co-Authors: Gregory F Cooper, Edward HerskovitsAbstract:This paper presents a Bayesian Method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic Method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the Methods in this paper to previous work, and we discuss open problems.
T Theo A Arentze - One of the best experts on this subject based on the ideXlab platform.
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adaptive personalized travel information systems a Bayesian Method to learn users personal preferences in multimodal transport networks
IEEE Transactions on Intelligent Transportation Systems, 2013Co-Authors: T Theo A ArentzeAbstract:Providing personalized advice is an important objective in the development of advanced traveler information systems. In this paper, a Bayesian Method to incorporate learning of users' personal travel preferences in a multimodal routing system is proposed. The system learns preference parameters incrementally based on travel choices a user makes. Existing Bayesian inference Methods require too much computation time for the learning problem that we are dealing with here. Therefore, an approximation Method is developed, which is based on sequential processing of preference parameters and systematic sampling of the parameter space. The data of repetitive travel choices of a representative sample of individuals are used to test the system. The results indicate that the system rapidly adapts to a user and learns his or her preferences effectively. The efficiency of the algorithm allows the system to handle realistically sized learning problems with short response times even when many users are to be simultaneously processed. It is therefore concluded that the approach is feasible; problems for future research are identified.
