نتایج جستجو برای: bayes networks

تعداد نتایج: 444659  

2015
Bo Yang Xuehua Zhao Xueyan Liu

There has been an increasing interest in exploring signed networks with positive and negative links in that they contain more information than unsigned networks. As fundamental problems of signed network analysis, community detection and sign (or attitude) prediction are still primary challenges. To address them, we propose a generative Bayesian approach, in which 1) a signed stochastic blockmo...

2015
Sanjeev Dhawan Meena Devi

Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge t...

1996
Dan Geiger David Heckerman Christopher Meek

We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian ne...

1995
Wray L. Buntine

Chain graphs combine directed and undi­ rected graphs and their underlying mathe­ matics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, cluster­ ing with conditional interaction between vari­ able...

1998
Jesús Cerquides Ramon López de Mántaras

Comprehensibility is a key characteristic for learning algorithms results to be useful in Knowledge Discovery in Databases tasks. Bayesian reasoning has been usually criticized as hard to explain and understand, but achieves high performance rates with simple constructs, as happens for instance with the Naive-Bayes classifier. Our approach can be viewed as a refinement of qualitative probabilis...

1999
Dimitris Margaritis Sebastian Thrun

In recent years, Bayesian networks have become highly successful tool for diagnosis, analysis, and decision making in real-world domains. We present an efficient algorithm for learning Bayes networks from data. Our approach constructs Bayesian networks by first identifying each node’s Markov blankets, then connecting nodes in a maximally consistent way. In contrast to the majority of work, whic...

2017
Long Jin Justin Lazarow Zhuowen Tu

We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN tries to iteratively: (1) synthesize pseudo-negative samples; and (2) enhance itself by improving the ...

2006
Aleks Jakulin Irina Rish

We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend näıve Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar complexity. Our Markov network model is represented as a set of consistent probability distributions on subsets of variables. Inference with such a mod...

2003
Rafael Timóteo de Sousa Júnior Alexandre C. V. de Oliveira Regina Tsujiguchi Vinícius M. Pacheco Cláudia Jacy Barenco Abbas Ricardo Staciarini Puttini Robson de Oliveira Albuquerque

With the consolidation of the Internet and other computer networks, the need of sophisticated systems to retrieve information increased enormously. In these networks, information is distributed on several machines and sometimes in different formats. In this context, we propose an architecture that besides retrieving information can classify it according to some criteria. To implement the propos...

2012
Andrew Gordon Wilson David A. Knowles Zoubin Ghahramani

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive dis...

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