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38
Nonlinear modeling of a production process by hybrid Bayesian Networks
 In: Werner Horn (Ed.): ECAI 2000
, 2000
"... This paper shows how nonlinear functions can be approximated by hybrid Bayesian networks. The basic idea is to make a piecewise linear approximation with several base points. This approach is applied to an engineering domain and the accuracy is compared to Gibbs sampling. Great accuracy is shown ev ..."
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This paper shows how nonlinear functions can be approximated by hybrid Bayesian networks. The basic idea is to make a piecewise linear approximation with several base points. This approach is applied to an engineering domain and the accuracy is compared to Gibbs sampling. Great accuracy is shown even at noncontinuous functions. Due to the general underlying principle, it is possible to adapt this type of network to other domains.
Utilization of hierarchical stochastic relationship modeling for Hangul character recognition
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by ..."
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Abstract—In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by which strokes as well as relationships are stochastically represented by utilizing the hierarchical characteristics of target characters. A character is defined by a multivariate random variable over the components and its probability distribution is learned from a training data set. To overcome difficulties of the learning due to the high order of the probability distribution (a problem of curse of dimensionality), the probability distribution is factorized and approximated by a set of lowerorder probability distributions by applying the idea of relationship decomposition recursively to components and subcomponents. Based on the proposed method, a handwritten Hangul (Korean) character recognition system is developed. Recognition experiments conducted on a public database show the effectiveness of the proposed relationship modeling. The recognition accuracy increased by 5.5 percent in comparison to the most successful system ever reported. Index Terms—Pattern recognition, handwritten character recognition, stochastic relationship modeling, hierarchical character representation, Hangul character recognition. æ
Decentralised Data Fusion: A Graphical Model Approach
"... Abstract – This paper proposes the use of graphical models to describe decentralised data fusion systems. The task of decentralised data fusion is considered as a specific instance of the general distributed inference problem in which there is a single common state of interest which is (partially) o ..."
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Abstract – This paper proposes the use of graphical models to describe decentralised data fusion systems. The task of decentralised data fusion is considered as a specific instance of the general distributed inference problem in which there is a single common state of interest which is (partially) observed by a number of sensor platforms. Our objective is to model and solve this problem using standard graphical model techniques. Two options for modeling the problem are considered. The model based on distributed variable cliques is found superior to a graphical model with cloned variables. The model and the messages arising through inference are compared with the wellknown Channel Filter algorithm. Our approach to inference is to apply a distributed version of the Junction Tree algorithm developed by Paskin and Guestrin. The algorithms were validated in a series of simulated tracking problems.
A unified probabilistic framework for facial activity modeling and understanding,” For Peer Review Only
 Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition
, 2007
"... Facial activities are the most natural and powerful means of human communication. Spontaneous facial activity is characterized by rigid head movements, nonrigid facial muscular movements, and their interactions. Current research in facial activity analysis is limited to recognizing rigid or nonrig ..."
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Facial activities are the most natural and powerful means of human communication. Spontaneous facial activity is characterized by rigid head movements, nonrigid facial muscular movements, and their interactions. Current research in facial activity analysis is limited to recognizing rigid or nonrigid motion separately, often ignoring their interactions. Furthermore, although some of them analyze the temporal properties of facial features during facial feature extraction, they often recognize the facial activity statically, ignoring the dynamics of the facial activity. In this paper, we propose to explicitly exploit the prior knowledge about facial activities and systematically combine the prior knowledge with image measurements to achieve an accurate, robust, and consistent facial activity understanding. Specifically, we propose a unified probabilistic framework based on the dynamic Bayesian network (DBN) to simultaneously and coherently represent the rigid and nonrigid facial motions, their interactions, and their image observations, as well as to capture the temporal evolution of the facial activities. Robust computer vision methods are employed to obtain measurements of both rigid and nonrigid facial motions. Finally, facial activity recognition is accomplished through a probabilistic inference by systemically integrating the visual measurements with the facial activity model. 1.
Control of Dynamic Systems Using Bayesian Networks
 Proceedings of the IBERAMIA/SBIA 2000 Workshops (Atibaia, Sdo Paulo
, 2000
"... Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of artificial intelligence, machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well known since many ye ..."
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Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of artificial intelligence, machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well known since many years, Bayesian networks have not been applied to problems in the area of adaptive control of dynamic systems.
Shielding against conditioning side effects in graphical models.
 UBC LIBRARY
, 2005
"... When modelling uncertain beliefs with graphical models we are often presented with “natural ” distributions that are hard to specify. An example is a distribution of which instructor is teaching a course when we know that someone must teach it. Such distributions over a set of nodes can be easily de ..."
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When modelling uncertain beliefs with graphical models we are often presented with “natural ” distributions that are hard to specify. An example is a distribution of which instructor is teaching a course when we know that someone must teach it. Such distributions over a set of nodes can be easily described if we condition on a child of these nodes as part of the specification. This conditioning is not an observation of a variable in the real world but by fixing the value of the node, existing inference algorithms perform the calculations needed to achieve the desired distribution automatically. Unfortunately, although it achieves this goal it has side effects that we claim are undesirable. These side effects create dependencies between other variables in the model. This can lead to different beliefs throughout the model, including the constrained variables, than would otherwise be expected if the constraint is meant to be local in its effect. We describe the use of conditioning for these types of distributions and illuminate the problem of side effects, which have received little attention in the literature. We then present a method that still allows specification of these distributions easily using conditioning but counterbalancing side effects by adding other nodes to the network. iii
Learning Dynamic Bayesian NETWORKS FOR MULTIMODAL SPEAKER DETECTION
, 2000
"... Design and development of novel humancomputer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous multisensory data such as video and sound. Temporal fusion of multiple sensors is formulated using dynamic Bayesian network ..."
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Design and development of novel humancomputer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous multisensory data such as video and sound. Temporal fusion of multiple sensors is formulated using dynamic Bayesian networks (DBNs) which allow the power of statistical inference and learning to be combined with contextual knowledge of the problem. Mismatch between classifier models and true data distributions on one hand and the use of approxiamte inference methods on the other hand all contribute to inaccurarte classification. Recent work on boosting by Schapire et al. and additive probabilitic models by Hastie et al. have shown that improved classification can be obtained by combining a number of simple (naive) classifiers. Building upon this spirit, we formulate a learning framework for DBNs based on errorfeedback and statistical boosting theory. Another improvement is obtained by extending this idea to use...
Inference in probabilistic logic programs with continuous random variables, Theory Pract
 Log. Program
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