Neural Networks
Irregularity in multi-dimensional space-filling curves with applications in multimedia databases
Proceedings of the tenth international conference on Information and knowledge management
Cross-validation in Fuzzy ARTMAP for large databases
Neural Networks
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Alternative Algorithm for Hilbert's Space-Filling Curve
IEEE Transactions on Computers
Fast learning in networks of locally-tuned processing units
Neural Computation
On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Classification of noisy signals using fuzzy ARTMAP neural networks
IEEE Transactions on Neural Networks
μARTMAP: use of mutual information for category reduction in Fuzzy ARTMAP
IEEE Transactions on Neural Networks
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Fuzzy Sets and Systems
All-nearest-neighbors finding based on the Hilbert curve
Expert Systems with Applications: An International Journal
A pseudo-hilbert scan algorithm for arbitrarily-sized rectangle region
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
| Hi-index | 0.00 |
The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems.