Competitive learning algorithms for vector quantization
Neural Networks
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expansive and Competitive Learning for Vector Quantization
Neural Processing Letters
Expansive and Competitive Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
A Neighborhood-Based Competitive Network for Video Segmentation and Object Detection
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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In this work, a general framework for developing learning rules with an added term (perturbation term) is presented. Many learning rules commonly cited in the specialized literature can be derived from this general framework. This framework allows us to introduce some knowledge about vector quantization (as an optimization problem) in the distortion function in order to derive a new learning rule that uses that information to avoid certain local minima of the distortion function, leading to better performance than classical models. Computational experiments in image compression show that our proposed rule, derived from this general framework, can achieve better results than simple competitive learning and other models, with codebooks of less distortion.