In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent causal relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using K2 algorithm combined with GA (genetic algorithm). In th...

In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent the conditional dependence relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using the K2 algorithm combined with a gen...

The challenge of agility for adopting new business norms creates the need for measuring performance under changing conditions. This study aims to demonstrate the financial and non-financial consequences of implementing different combinations of lean techniques on the business performance. Bayesian Belief Network is used in studying the effects of factors under changing conditions. There are sev...

In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent causal relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using K2 algorithm combined with GA (genetic algorithm). In th...

Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven questionanswering (cQA) dataset and the forum dataset, we present a novel learning strat...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...

Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper, we address this issue and demonstrate how the ...

In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks...

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in sy...

Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should co...