International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Neural Networks
Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Inference for the Generalization Error
Machine Learning
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Immune network based ensembles
Neurocomputing
Variants of Memetic And Hybrid Learning of Perceptron Networks
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Image Processing
Fuzzy Evolutionary Probabilistic Neural Networks
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Neural Network Ensembles from Training Set Expansions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An ensemble of degraded neural networks
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Musical composer identification through probabilistic and feedforward neural networks
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Parallel and local learning for fast probabilistic neural networks in scalable data mining
Proceedings of the 6th Balkan Conference in Informatics
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In this contribution, novel approaches are proposed for the improvement of the performance of Probabilistic Neural Networks as well as the recently proposed Evolutionary Probabilistic Neural Networks. The Evolutionary Probabilistic Neural Network's matrix of spread parameters is allowed to have different values in each class of neurons, resulting in a more flexible model that fits the data better and Particle Swarm Optimization is also employed for the estimation of the Probabilistic Neural Networks's prior probabilities of each class. Moreover, the bagging technique is used to create an ensemble of Evolutionary Probabilistic Neural Networks in order to further improve the model's performance. The above approaches have been applied to several well-known and widely used benchmark problems with promising results.