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Risk prediction for weed infestation using classification rules
(2009-12-01)
This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from ...
Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks
(Bellevue, 2013-12-04)
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provade a very Complexity of inferences in polytree-shaped ...
Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning
(2002-12-01)
In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by ...
AI Sport Forecast Software
This article aims to explain the development of an application whose function is to predict the results of different sporting encounters. To do this an analysis of the influential factors, algorithms and technology ...
Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop
(PERGAMON-ELSEVIER SCIENCE LTD, 2009)
This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier ...
Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique
methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of ...
Data Fusion through Fuzzy-Bayesian Networks for Belief Generation in Cognitive Agents
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2019)
Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training
Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. ...