Displaying similar documents to “Building adaptive tests using Bayesian networks”

Completing an uncertainty criterion of classification.

Joaquín Abellán (2005)

Mathware and Soft Computing

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We present a variation of a method of classification based in uncertainty on credal set. Similarly to its origin it use the imprecise Dirichlet model to create the credal set and the same uncertainty measures. It take into account sets of two variables to reduce the uncertainty and to seek the direct relations between the variables in the data base and the variable to be classified. The success are equivalent to the success of the first method except in those where there are a direct...

IctNeo system for jaundice management

C. Bielza, M. Gómez, S. Ríos-Insua, J. A. Fernández Del Pozo, P. García Barreno, S. Caballero, M. Sánchez Luna (1998)

Revista de la Real Academia de Ciencias Exactas Físicas y Naturales

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Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Luis M. de Campos, José A. Gámez, José M. Puerta (2002)

Mathware and Soft Computing

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The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used. A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied...

KIS: An automated attribute induction method for classification of DNA sequences

Rafał Biedrzycki, Jarosław Arabas (2012)

International Journal of Applied Mathematics and Computer Science

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This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites....

A method for learning scenario determination and modification in intelligent tutoring systems

Adrianna Kozierkiewicz-Hetmańska, Ngoc Thanh Nguyen (2011)

International Journal of Applied Mathematics and Computer Science

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Computers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning path in each step of the educational process. The determination of a suitable learning path depends on the student's...

Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems

Grzegorz Chmaj, Krzysztof Walkowiak, Michał Tarnawski, Michał Kucharzak (2012)

International Journal of Applied Mathematics and Computer Science

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Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational...

Learning the naive Bayes classifier with optimization models

Sona Taheri, Musa Mammadov (2013)

International Journal of Applied Mathematics and Computer Science

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Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three...