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Efficient Multiclass Object Detection by a Hierarchy of Classifiers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An ontology-based knowledge management system for flow and water quality modeling
Advances in Engineering Software
Computers in Biology and Medicine
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Pattern Recognition Letters
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
ICSC '07 Proceedings of the International Conference on Semantic Computing
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Fuzzy Sets and Systems
Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging
IEEE Transactions on Knowledge and Data Engineering
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
FR3: a fuzzy rule learner for inducing reliable classifiers
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Good error correcting output codes for adaptive multiclass learning
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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A multi-viewpoint system to support abductive reasoning
Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
Paraconsistent artificial neural network: applicability in computer analysis of speech productions
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Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
Fuzzy goal programming - A parametric approach
Information Sciences: an International Journal
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This paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency.