The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
A new adaptive polynomial neural network
MIM-S2 '93 Papers from the symposium on Second mathematical and intelligent models in system simulation
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computational Geometry for Design and Manufacture
Computational Geometry for Design and Manufacture
A comparison of fuzzy shell-clustering methods for the detection of ellipses
IEEE Transactions on Fuzzy Systems
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
The Journal of Machine Learning Research
Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index
Neural Processing Letters
Multiorder neurons for evolutionary higher-order clustering and growth
Neural Computation
| Hi-index | 0.00 |
This article introduces a method for clustering irregularly shaped data arrangements using high-order neurons. Complex analytical shapes are modeled by replacing the classic synaptic weight of the neuron by high-order tensors in homogeneous coordinates. In the first- and second-order cases, this neuron corresponds to a classic neuron and to an ellipsoidalmetric neuron. We show how high-order shapes can be formulated to follow the maximum-correlation activation principle and permit simple local Hebbian learning. We also demonstrate decomposition of spatial arrangements of data clusters, including very close and partially overlapping clusters, which are difficult to distinguish using classic neurons. Superior results are obtained for the Iris data.