Results 1  10
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32
Hidden process models
 In International Conference of Machine Learning ICML
, 2006
"... We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, hig ..."
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Cited by 10 (4 self)
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We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, highdimensional, nonMarkovian, and often involves prior knowledge of the form “hidden event A occurs n times within the interval [t,t ′]. ” HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.
Improving Bayesian Network Parameter Learning using Constraints
"... This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is general in the sense that any convex constraint is allowed, which includes many proposals in the literature. Driven by a ma ..."
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Cited by 9 (1 self)
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This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is general in the sense that any convex constraint is allowed, which includes many proposals in the literature. Driven by a maximum entropy criterion and the Imprecise Dirichlet Model, we present a constrained convex optimization formulation to combine priors, constraints and data. Experiments indicate benefits of this framework. 1
Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce
"... Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for incomplete data. Applying the EM algorithm to learn BN parameters is unfortunately susceptible to local optima and prone to premature convergence. We develop and experiment with two methods for ..."
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Cited by 8 (1 self)
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Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for incomplete data. Applying the EM algorithm to learn BN parameters is unfortunately susceptible to local optima and prone to premature convergence. We develop and experiment with two methods for improving EM parameter learning by using MapReduce: AgeLayered Expectation Maximization (ALEM) and Multiple Expectation Maximization (MEM). Leveraging MapReduce for distributed machine learning, these algorithms (i) operate on a (potentially large) population of BNs and (ii) partition the data set as is traditionally done with MapReduce machine learning. For example, we achieved gains using the Hadoop implementation of MapReduce in both parameter quality (likelihood) and number of iterations (runtime) using distributed ALEM on for the BN Asia over 20,000 MEM and ALEM trials. 1
Classification in Very High Dimensional Problems with Handfuls of Examples
"... Abstract. Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Baye ..."
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Cited by 7 (0 self)
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Abstract. Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class. 1
A new Parameter Learning Method for Bayesian Networks with Qualitative Influences
 UAI
, 2007
"... We propose a new method for parameter learning in Bayesian networks with qualitative influences. This method extends our previous work from networks of binary variables to networks of discrete variables with ordered values. The specified qualitative influences correspond to certain order restriction ..."
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Cited by 4 (1 self)
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We propose a new method for parameter learning in Bayesian networks with qualitative influences. This method extends our previous work from networks of binary variables to networks of discrete variables with ordered values. The specified qualitative influences correspond to certain order restrictions on the parameters in the network. These parameters may therefore be estimated using constrained maximum likelihood estimation. We propose an alternative method, based on the isotonic regression. The constrained maximum likelihood estimates are fairly complicated to compute, whereas computation of the isotonic regression estimates only requires the repeated application of the Pool Adjacent Violators algorithm for linear orders. Therefore, the isotonic regression estimator is to be preferred from the viewpoint of computational complexity. Through experiments on simulated and real data, we show that the new learning method is competitive in performance to the constrained maximum likelihood estimator, and that both estimators improve on the standard estimator.
Evaluation Results for a QueryBased Diagnostics Application
"... QueryBased Diagnostics refers to the simultaneous building and use of Bayes network diagnostic models, removing the distinction between elicitation and inference phases. In this paper we describe a successful eld trial of such a system in manufacturing. The detailed session logs that are collected ..."
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Cited by 4 (2 self)
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QueryBased Diagnostics refers to the simultaneous building and use of Bayes network diagnostic models, removing the distinction between elicitation and inference phases. In this paper we describe a successful eld trial of such a system in manufacturing. The detailed session logs that are collected during use of the system reveal how the model evolved in use, and pose challenging questions on how the models can be adapted to session outcomes. 1
Domain knowledge uncertainty and probabilistic parameter constraints
 In Proceedings of Twenty Fifth Conference on Uncertainty in Artificial Intelligence (UAI
, 2009
"... Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints o ..."
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Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy. We focus on generative and conditional modeling where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate the framework with experiments on both synthetic and realworld data. 1
DataFree Prior Model for Facial Action Unit Recognition
"... Abstract—Facial action recognition is concerned with recognizing the local facial motions from image or video. In recent years, besides the development of facial feature extraction techniques and classification techniques, prior models have been introduced to capture the dynamic and semantic relatio ..."
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Abstract—Facial action recognition is concerned with recognizing the local facial motions from image or video. In recent years, besides the development of facial feature extraction techniques and classification techniques, prior models have been introduced to capture the dynamic and semantic relationships among facial action units. Previous works have shown that combining the prior models with the image measurements can yield improved performance in AU recognition. Most of these prior models, however, are learned from data, and their performance hence largely depends on both the quality and quantity of the training data. These datatrained prior models cannot generalize well to new databases, where the learned AU relationships are not present. To alleviate this problem, we propose a knowledgedriven prior model for AU recognition, which is learned exclusively from the generic domain knowledge that governs AU behaviors, and no training data are used. Experimental results show that, with no training data but generic domain knowledge, the proposed knowledgedriven model achieves comparable results to the datadriven model for specific database and significantly outperforms the datadriven models when generalizing to new data set. Index Terms—Facial action units recognition, Bayesian networks, knowledgedriven model Ç 1
Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition
"... Abstract. Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quality of network parameters. Learning reliable parameters of Bayesian networks often requires a large amount of t ..."
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Abstract. Probabilistic graphical models such as Bayesian Networks have been increasingly applied to many computer vision problems. Accuracy of inferences in such models depends on the quality of network parameters. Learning reliable parameters of Bayesian networks often requires a large amount of training data, which may be hard to acquire and may contain missing values. On the other hand, qualitative knowledge is available in many computer vision applications, and incorporating such knowledge can improve the accuracy of parameter learning. This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation. For complete data, a global optimum solution to maximum likelihood estimation is obtained in polynomial time, while for incomplete data, a modified expectationmaximization method is proposed. This framework is applied to real image data from a facial action unit recognition problem and produces results that are similar to those of stateoftheart methods. 1
Combining subjective probabilities and data in training markov logic networks
 of Lecture Notes in Computer Science
, 2012
"... Abstract. Markov logic is a rich language that allows one to specify a knowledge base as a set of weighted firstorder logic formulas, and to define a probability distribution over truth assignments to ground atoms using this knowledge base. Usually, the weight of a formula cannot be related to the ..."
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Abstract. Markov logic is a rich language that allows one to specify a knowledge base as a set of weighted firstorder logic formulas, and to define a probability distribution over truth assignments to ground atoms using this knowledge base. Usually, the weight of a formula cannot be related to the probability of the formula without taking into account the weights of the other formulas. In general, this is not an issue, since the weights are learned from training data. However, in many domains (e.g. healthcare, dependable systems, etc.), only little or no training data may be available, but one has access to a domain expert whose knowledge is available in the form of subjective probabilities. Within the framework of Bayesian statistics, we present a formalism for using a domain expert’s knowledge for weight learning. Our approach defines priors that are different from and more general than previously used Gaussian priors over weights. We show how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge. Additionally, we also provide a formalism for capturing conditional subjective probabilities, which are often easier to obtain and more reliable than nonconditional probabilities. We demonstrate the effectiveness of our approach by extensive experiments in a domain that models failure dependencies in a cyberphysical system. Moreover, we demonstrate the advantages of using our proposed prior over that of using nonzero mean Gaussian priors in a commonly cited social network MLN testbed. 1