Scalable Model for Extensional and Intensional Descriptions of Unclassified Data
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Discovering Knowledge from Meteorological Databases: A Meteorological Aviation Forecast Study
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Accuracy Tuning on Combinatorial Neural Model
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
An Intelligent Decision Support Model for Aviation Weather Forcasting
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
On the Computational Power of Max-Min Propagation Neural Networks
Neural Processing Letters
A reasoning-based strategy for exploring the synergy among alternative crops
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Characterizing human features: An applied study on proactivity perception of undergraduate students
Pattern Recognition and Image Analysis
Human features recognition with CNM: An applied study concerning undergraduate students
Pattern Recognition and Image Analysis
| Hi-index | 0.00 |
The combinatorial neural model (CNM) is a type of fuzzy neural network for classification problems. Learning in CNM is a complex task spanning the learning of input-neuron membership functions, the network topology and connection weights. We deal with these various aspects of learning in CNM, most notably with the learning of connection weights, whose complexity comes from the existence of nondifferentiable, nonconvex error functions associated with the learning process. We introduce several algorithms for weight learning. All the algorithms are based on “local” rules, and are therefore amenable to distributed/parallel implementations. Experimental results are provided on the large-scale problem of monitoring the deforestation of the Amazon region on satellite images. These results show that a hybrid CNM system outperforms previous results obtained with variations of error backpropagation techniques. In addition, this hybrid system has demonstrated robustness in the context under consideration