Results 1  10
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21
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 9 (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.
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 5 (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 3 (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
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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Cited by 2 (2 self)
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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
Automated Refinement of Bayes Networks’ Parameters based on Test Ordering Constraints
"... In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test’s relative diagnostic value. We demonstrate that consistency with ..."
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Cited by 1 (1 self)
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In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test’s relative diagnostic value. We demonstrate that consistency with an expert’s test selection leads to nonconvex constraints on the model parameters. We incorporate these constraints by augmenting the network with nodes that represent the constraint likelihoods. Gibbs sampling, stochastic hill climbing and greedy search algorithms are proposed to find a MAP estimate that takes into account test ordering constraints and any data available. We demonstrate our approach on diagnostic sessions from a manufacturing scenario. 1
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 1 (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 1 (0 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
Decoding Brain Activity Using the ZeroShot Learning Model
, 2011
"... Machine learning algorithms have been successfully applied to learning classifiers in many domains such as computer vision, fraud detection, and brain image analysis. Typically, classifiers are trained to predict a class value given a set of labeled training data that includes all possible class val ..."
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Machine learning algorithms have been successfully applied to learning classifiers in many domains such as computer vision, fraud detection, and brain image analysis. Typically, classifiers are trained to predict a class value given a set of labeled training data that includes all possible class values, and sometimes additional unlabeled training data. Little research has been performed where the possible values for the class variable include values that have been omitted from the training examples. This is an important problem setting, especially in domains where the class value can take on many values, and the cost of obtaining labeled examples for all values is high. We show that the key to addressing this problem is not predicting the heldout classes directly, but rather by recognizing the semantic properties of the classes such as their physical or functional attributes. We formalize this method as zeroshot learning and show that by utilizing semantic knowledge mined from large text corpora
Weather Forecasting with Bayesian Networks Causal Modelling
"... objective of the weather model was to investigate the methods and effects of forecasting weather for stations in Southern Africa. Furthermore it consisted of three sections namely, Causal Modelling, Dynamic Bayesian Network Learning, and a Visualization System. This report focuses on causal modellin ..."
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objective of the weather model was to investigate the methods and effects of forecasting weather for stations in Southern Africa. Furthermore it consisted of three sections namely, Causal Modelling, Dynamic Bayesian Network Learning, and a Visualization System. This report focuses on causal modelling. The Naive Bayes, K2, LK2, and Greedy Thick Thinning algorithms were implemented and evaluated. The results show that the Naive Bayes algorithm constructs networks in the shortest time but with the lowest predictive accuracy. The networks produced by the LK2 algorithm forecasted with the highest predictive accuracy when using precipitation data, while the Greedy Thick Thinning algorithm produced the highest predictive accuracy networks for minimum and maximum temperature data. Keywords: Graph algorithms, Computations on discrete structures.