A common approach to learning Bayesian networks involves specifying an appropriately chosen family of parameterized probability density such as Gaussian. However, the distribution of most real-life data is leptokurtic...
详细信息
A common approach to learning Bayesian networks involves specifying an appropriately chosen family of parameterized probability density such as Gaussian. However, the distribution of most real-life data is leptokurtic and may not necessarily be best described by a Gaussian process. In this work we introduce Cauchy graphicalmodels (CGM), a class of multivariate Cauchy densities that can be represented as directed acyclic graphs with arbitrary network topologies, the edges of which encode linear dependencies between random variables. We develop CGLearn, the resultant algorithm for learning the structure and Cauchy parameters based on Minimum Dispersion Criterion (MDC). Experiments using simulated datasets on benchmark network topologies demonstrate the efficacy of our approach when compared to Gaussian graphicalmodels (GGM).
The Joint Directors of Laboratories fusion model is adequate as a functional description, but falls short as a formal design guide for the application of logical inference under uncertainty for high-level information ...
详细信息
ISBN:
(纸本)9781479916344
The Joint Directors of Laboratories fusion model is adequate as a functional description, but falls short as a formal design guide for the application of logical inference under uncertainty for high-level information fusion. We propose a formal construct called the Scientific Inquiry Fusion Theory, with three stages of explanation, prediction and generalisation aligned with the corresponding inferences of abduction, deduction and induction. We first define fusion as formal models without uncertainty, in which the corresponding logical inference patterns can be used for solving fusion problems. Then, we extend these formal models with uncertainty through probabilistic graphical models, where fusion processes are realised by statistical queries and learning algorithms based on the sound unification of Probability Theory, Mathematical Logic and Machine Learning. Finally, we demonstrate the application of this formal high-level information fusion framework with an example of automated maritime security situation and threat assessment.
Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks' handling is the model structure training. ...
详细信息
ISBN:
(数字)9783030868550
ISBN:
(纸本)9783030868550;9783030868543
Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks' handling is the model structure training. This paper is dedicated to the automation of this process for algebraic Bayesian networks. This work relates to the PC-algorithm for algebraic Bayesian network secondary structure training. The algorithm is based on the PC-algorithm for Belief Bayesian networks training. The algorithm pseudocode and usage example are described. The provided algorithm helps investigate the full-automated machine learning of algebraic Bayesian networks. Earlier, the structure was provided by experts.
Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve ...
详细信息
ISBN:
(纸本)9780769536415
Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.
We discuss the applicability of large quantum annealers for the purpose of processing Remote Sensing images. We show an application of currently existing quantum annealers for the purpose of post-processing segmentati...
详细信息
ISBN:
(纸本)9798350360332;9798350360325
We discuss the applicability of large quantum annealers for the purpose of processing Remote Sensing images. We show an application of currently existing quantum annealers for the purpose of post-processing segmentation of a hyperspectral image. We show that in principle there might exist useful applications of large scale quantum annealers for the purpose of Remote Sensing data processing.
We consider a probabilisticgraphical model for the problem of tracking entities moving among a finite set of sites. The observations consist of counts of the number of entities at sites and during movement between si...
详细信息
ISBN:
(纸本)9781509061679
We consider a probabilisticgraphical model for the problem of tracking entities moving among a finite set of sites. The observations consist of counts of the number of entities at sites and during movement between sites. A Bayesian approach is adopted and an importance sampling approach taken to obtaining samples from the model. A backtrack-free proposal distribution is considered and an oracle is obtained through the construction of appropriate network flow problems.
This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the cla...
详细信息
ISBN:
(纸本)9781509049516
This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipua, Sao Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.
When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class mu...
详细信息
ISBN:
(纸本)9781450355520
When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class multi-label annotation. To improve the quality of labels, one task is independently completed by a group of heterogeneous crowdsourced workers. Then, the true values of the multiple labels of each task are inferred from these repeated noisy labels. In this paper, we propose a novel probabilistic method, which includes a multi-class multi-label dependency (MCMLD) model, to address this problem. The proposed method assumes that the label-correlation exists in both unknown true labels and noisy crowdsourced labels. Thus, it introduces a mixture of multiple independently multinoulli distributions to capture the correlation among the labels. Finally, the unknown true values of the multiple labels of each task, together with a set of confusion matrices modeling the reliability of the workers, can be jointly inferred through an EM algorithm. Experiments with three simulated typical crowdsourcing scenarios and a real-world dataset consistently show that our proposed MCMLD method significantly outperforms several competitive alternatives. Furthermore, if the labels are strongly correlated, the advantage of MCMLD will be more remarkable.
The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning Bayesian networks from data. However, common formulations of those Bayesian score metrics ...
详细信息
The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning Bayesian networks from data. However, common formulations of those Bayesian score metrics rely on free parameters which are hard to assess. Recent theoretical and experimental works have also shown that the commonly employed BDe score metric is strongly biased by the particular assignments of its free parameter known as the equivalent sample size. This sensitivity means that poor choices of this parameter lead to inferred BN models whose structure and parameters do not properly represent the distribution generating the data even for large sample sizes. In this paper we argue that the problem is that the BDe metric is based on assumptions about the BN model parameters distribution assumed to generate the data which are too strict and do not hold in real settings. To overcome this issue we introduce here an approach that tries to marginalize the meta-parameter locally, aiming to embrace a wider set of assumptions about these parameters. It is shown experimentally that this approach offers a robust performance, as good as that of the standard BDe metric with an optimum selection of its free parameter and, in consequence, this method prevents the choice of wrong settings for this widely applied Bayesian score metric. (C) 2012 Elsevier Inc. All rights reserved.
Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching wei...
详细信息
ISBN:
(纸本)9781450343916
Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to a wide variety of problems in natural language understanding, vision, computational biology, social networks and others, and is the basis of the open-source Alchemy system.
暂无评论