Competitive learning algorithms for vector quantization
Neural Networks
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Self-splitting competitive learning: a new on-line clustering paradigm
IEEE Transactions on Neural Networks
Theoretical Study on the Capacity of Associative Memory with Multiple Reference Points
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
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IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
K-pages graph drawing with multivalued neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Shortest common superstring problem with discrete neural networks
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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In a competitive neural network, a process unit (node) in the competitive layer is completely described by the vector of weight from the input node to it. Each such weight vector becomes the centroid of a cluster of inputs since the principal function of a competitive learning network is discovers cluster of overlapping input. In this paper we propose a competitive neural network where each process unit has a couple of weight vectors (dipoles) that becomes a line segment as representation of a cluster. A weight update is formulated such that the dipole associated with each process unit is as near as possible to all the input samples for which the node is the winner of the competition. This network allows the formation of groups or categories by means of unsupervised learning, where each class or category is identified by a line segment instead of a centroid. The line segment leads to a better representation of a group or class that a centroid that gives us only the position of the cluster. The network has been applied to the formation of groups or categories using the data IRIS, where the unsupervised learning algorithms reach between 12 and 17 incorrect classifications. However, while many partitional clustering algorithms and competitive neural networks are only suitable for detecting hyperspherical-shaped clusters, the proposed network gets only 5 incorrect classifications and is also suitable for detecting hyperspherical-shaped clusters.