bayesian network 
Application of bayesian network learning methods to land resource evaluation


All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.


Uncertainty modeling based on Bayesian network in ontology mapping


Research on Bayesian network based user's interest model


On the basis of analyzing the existing users' interest models and some basic questions of users' interest (representation, derivation and identification of users' interest), a Bayesian network based users' interest model is given.


In this model, the users' interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users' interested and not interested documents are used to train the Bayesian network.


Sensor planning for mobile robot localization using Bayesian network inference


We propose a new method of sensor planning for mobile robot localization using Bayesian network inference.


This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning.


However, to construct a Bayesian network that fits a given dataset is a NPhard problem, and it also needs consuming mass computational resources.


A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images.


In it, we will present an application based on construction of a Bayesian network from a database of financial reports collected for the years 199397.


We will focus on one sector of the Czech economy  engineering  presenting an example that use the constructed Bayesian network in the sector financial analysis.


A hierarchical Bayesian network for event recognition of human actions and interactions


This paper presents a method for the recognition of twoperson interactions using a hierarchical Bayesian network (BN).


The evolution of the poses of the multiple body parts are processed by a dynamic Bayesian network (DBN).


Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other uses.


A Bayesian network dynamic model was developed to determine the kinematics of the intervertebral joints of the lumbar spine.


In this paper we present the DempsterShafer theory as a framework within which the results of a Bayesian network classifier and a fuzzy logicbased classifier are combined to produce a better final classification.


Detection of surface defects on raw steel blocks using Bayesian network classifiers

