The objective of this paper is to describe the use of a probabilistic approach to Web effort estimation by means of a Bayesian Network. A Bayesian Network is a model that embodies ...
— For a robot to understand a scene, we have to infer and extract meaningful information from vision sensor data. Since scene understanding consists in recognizing several visual...
We address document image classification by visual appearance. An image is represented by a variable-length list of visually salient features. A hierarchical Bayesian network is ...
This paper describes a mode detection system for online pen input that employs a Bayesian network to combine classification results and context information. Previous monolithic c...
— Bayesian networks play a key role in decision support within health care. Physicians rely on Bayesian networks to give medical treatment, generate what-if scenarios, and other ...
In this paper we propose a method for on-line max auditing of dynamic statistical databases. The method extends the Bayesian approach presented in [2], [3] and [4] for static data...
— In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera. O...
Bayesian networks are probabilistic graphical models widely employed in AI for the implementation of knowledge-based systems. Standard inference algorithms can update the beliefs a...
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
Association search is to search for certain instances in semantic web and then make inferences from and about the instances we have found. In this paper, we propose the problem of...