Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An experiment in linguistic synthesis with a fuzzy logic controller
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Effects of early and late nocturnal sleep on declarative and procedural memory
Journal of Cognitive Neuroscience
Expert Systems with Applications: An International Journal
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Cognitive Architectures: Where do we go from here?
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Fuzzy and neuro-fuzzy systems are increasingly among the key technologies employed in many real-world applications. However, traditional neuro-fuzzy systems are generally still lacking the scalability traits required in the face of large-scale data and the capability to incorporate new information without catastrophically disrupting the existing knowledge base. This work aims at addressing these issues by proposing a novel neuro-fuzzy system termed dual consolidation network (DCN) that models the complementary interactions between hippocampus and neocortex regions in the human brain to consolidate and exploit knowledge effectively. This approach allows the DCN to handle data sets with high-dimensional features and/or a very large number of samples efficiently, as well as to minimize interference when acquiring new information. Preliminary experiments employing DCN on large-scale biomedical data have shown encouraging results.