belief network
Elicitation of Bayesian Belief Network (EBBN) using Z-Number Approach
... Mechanism) network available in Netica (Norsys Software ...ALARM network is a complex belief network of medical diagnostic system for patient monitoring (Bein- lich et ...
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SOFTWARE DEFECT PREDICTION USING DEEP BELIEF NETWORK WITH L1 REGULARIZATION BASED OPTIMIZATION
... In this work, we have focused on software defect prediction techniques using deep learning technique and presented a novel approach for early bug prediction. In order to perform this task, we have considered deep ...
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Modelling and trading commodities with a new deep belief network
... The nodes on each layer have no connections between them only with units of other layers. All these connections are symmetric and bidirectional. As i mentioned before the restricted Boltzmann machines have been used in a ...
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Dynamic Bayesian belief network to model the development of walking and cycling schemes
... Bayesian belief network (BBN) has been found in many applications described in the literature (Jensen, 1996; Neil et ...a network in which arrows are pointing from parent nodes (causes) to child ...
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Examination of Bayesian belief network for safety assessment of nuclear computer-based systems
... Abstract We report here on a continuation of work on the Bayesian Belief Network (BBN) model described in [Fenton, Littlewood et al. 1998]. As explained in the previous deliver- able, our model concerns one ...
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Exploring Deep Belief Network for Chinese Relation Extraction
... Relation extraction is a fundamental task in information extraction that identifies the semantic relationships between two entities in the text. In this paper, a novel model based on Deep Belief Network ...
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Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network
... Bayesian Belief Networks (BBN) for developing a practical framework for machine learning process incorporating the commonsense ...Bayesian Belief Networks grant a systematic and localized method for ...
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Learning Semantics with Deep Belief Network for Cross Language Information Retrieval
... deep belief network (DBN)-based semantic analysis models for each language and a canonical correlation analysis (CCA) model for inter-lingual similarity ...
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Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network
... Bayesian belief networks (BBN) have proven to be computationally viable empirical probabilistic models of data ...Bayesian belief networks, particularly for classification and probabilistic reasoning ...
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Bayesian Belief Network, Bayesian Learning, Information Security, Intelligent Agent, Risk Assessment.
... Bayesian Belief Network is the choice because it could graphically represent the probabilistic relationships regarding to the data set which we ...the network and the probability estimation of each ...
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Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model
... deep belief network (DBN) model to evaluate the performance status of the ...neural network (BPNN)), the proposed CAM has been proved to be more sensitive to the incipient fault and more effective ...
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Combining Context Features by Canonical Belief Network for Chinese Part Of Speech Tagging
... Canonical Belief Network was proposed by Turtle in 1991(Turtle, 1991), and it was used in informa- tion retrieval tasks. Four canonical forms are pre- sented to combine different features, that is and, or, ...
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Speaker recognition with hybrid features from a deep belief network
... For speaker recognition task, a first attempt on the use of RBMs has been reported by [6]. They use a single RBM training and apply the model to a speaker verification task. They model pairs of i-vectors using Gaussian- ...
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Scalability and Portability of a Belief Network based Dialog Model for Different Application Domains
... a Belief Network (BN)-based mixed initiative dialog model across application ...The Belief Networks (BNs) are used to automatically govern the transitions between a system-initiative and a ...
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Self-organizing traffic flow prediction with an optimized deep belief network for internet of vehicles
... fast network connectivity is ...neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) ...
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A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia
... Bayesian Belief Network (BBN), incorporating a range of abiotic, biotic and anthropo- genic factors that might affect features such as the pop- ulation densities of Ciconiiformes and ...
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Intrusion detection for IoT based on improved genetic algorithm and deep belief network
... Abstract—With the advent of the Internet of Things, the security of the network layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well ...
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Automatic belief network modeling via policy inference for SDN fault localization
... obtain network statistics and populate the belief network with the prior probabilities of identifi- able components (both hardware and software) and their conditional probabilities of observable ...
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Deep Belief Network for Prediction of Rician Fading Channel
... Deep Belief Network and it a class of deep neural network consists of many hidden units, which are connected between the layers but not between the units within each layer, it is the first effective ...
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Bayesian belief network model for the safety assessment of nuclear computer-based systems
... The problem of safety assessment for complex systems involves a large number of variables and different potential sources of evidence. Many of these variables are inter-related by dependencies of an experimental, ...
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