Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
On the Use of Morphometry Based Features for Alzheimer's Disease Detection on MRI
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Results of an Adaboost Approach on Alzheimer's Disease Detection on MRI
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Classification results of artificial neural networks for Alzheimer's disease detection
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Computers in Biology and Medicine
Binary Image 2D Shape Learning and Recognition Based on Lattice-Computing (LC) Techniques
Journal of Mathematical Imaging and Vision
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
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From a practical industrial point of view parsimonious classifiers based on dendritic computing (DC) have two advantages: First they are implemented using only additive and min/max operators. They can be implemented in simple processors and be extremely fast providing classification responses. Second, parsimonious models improve generalization. In this paper we develop a formulation of dendritic classifiers based on lattice kernels and we train them using a direct Monte Carlo approach and a Sparse Bayesian Learning. We compare the results of both kinds of training with the relevance vector machines (RVM) on a collection of benchmark datasets.