Kohonen networks and Learning Vector Quantizers
Using Geometrical Properties for Fast Indexation of Gaussian Vector Quantizers
... of vector quantizers specially designed for Gaussian ...small networks (local) where quantization would be adapted in real time to the voice of the ...
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Predicting heat stressed EEG spectra by self organising feature map and learning vector quantizers——SOFM and LVQ based stress prediction
... of learning rate and neighborhood size, three neighborhood tapering schemes, and different number of training ...for learning rate and neighborhood size did not show any fixed trend such that conclusion ...
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One-Dimensional Kohonen Networks and Their Application to Automatic Classification of Images
... weight vector W j , 1 ≤ j ≤ m, is the most similar to the input point P k is chosen as winner neuron, for each t, 0 < t < ...by Kohonen, such selection is based on the squared Euclidean ...weight ...
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Dropout in Learning Vector Quantization Networks for Regularized Learning and Classification Confidence Estimation
... Machine Learning Group Center of Excellence Cognitive Interaction Technology Bielefeld University Machine learning methods promise to speed up and ease human decision making in various fields, such as ...
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CiteSeerX — Support-Vector Networks
... SUPPORT-VECTOR NETWORKS Corinna Cortes 1 and Vladimir Vapnik 2 AT&T Labs-Research, USA ...new learning machine for two-group classi cation ...the learning machine. The idea behind the ...
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Matrix Learning in Learning Vector Quantization
... A similar result has been derived in [11] for LVQ-networks as considered above which possess an adaptive diagonal metric. Remarkably, the margin is thereby directly cor- related to the nominator of the cost ...
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Matrix Learning in Learning Vector Quantization
... A similar result has been derived in [11] for LVQ-networks as considered above which possess an adaptive diagonal metric. Remarkably, the margin is thereby directly cor- related to the nominator of the cost ...
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Learning from distributed data sources using random vector functional-link networks
... machine learning context, distributed data, together with the requirements of preserving privacy and scaling up to large networks, brings the challenge of designing fully decentralized training ...
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Cooperative Avoidance Control-based Interval Fuzzy Kohonen Networks Algorithm in Simple Swarm Robots
... the learning process i n kohonen network is unsupervised, however that is taking a long time in the training proces, to find the weights that meet the good ...fast learning of sample sequences by the ...
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A Note on Learning Vector Quantization
... Abstract Vector Quantization is useful for data ...for vector quantization of unlabelled data. Vector quantization of labelled data for classification has a different objective, to minimize the ...
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An Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network
... Reinforcement Learning (RL) as a common method in this domain because of its trial-and-error nature does not have great performance in using transcendental ...Neural Networks (ANN) and specially ...
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Functional relevance learning in generalized learning vector quantization
... Barbara Hammer received her Ph.D. in Computer Science in 1995 and her venia legendi in Computer Science in 2003, both from the University of Osnab- rueck, Germany. From 2000 to 2004, she was leader of the junior research ...
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Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks
... weight vector was not considered valid if the as- sociated linear equations system could not be solved or if the 1-norm of the solution (the output-layer weights) was greater than a certain value M ...
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Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks
... Second, we have claimed that an alternative sequential method to construct SLFNs can be based on selecting the hidden-layer weights among the input vectors in the train- ing set. This method, which has been referred to ...
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Differential privacy for learning vector quantization
... the learning algorithm can be analyzed ...deep networks [30], a leakage of private information seems almost inevitable for interpretable models such as LVQ due to the fact that training data explicitly ...
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Average Competitive Learning Vector Quantization
... the learning phase: the ACLVQ is able to update more quantizers at each it- eration than the CLVQ and, at the same time, in the overall process the ACLVQ update less ...random vector simulations ...
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Statistical physics of learning vector quantization
... Cortes, C. and Vapnik, V.: 1995, Support-vector networks, Machine Learning 20(3), 273–297. Cottrell, M., Hammer, B., Hasenfuß, A. and Villmann, T.: 2006, Batch and median neural gas, Neural ...
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Applications of Support Vector-Based Learning
... feature vector that cannot be moved to a server or to other ...sensor networks or mobile ad hoc networks, the physical constraints on communication also prevent the collection of the ...
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Joint design of vector quantizers and RCPC channel codes for Rayleigh fading channels
... The VQ used in the iterative de- sign is a Channel-Optimized Vector Quantizer (COVQ) that has been optimized for the index crossover proba- bilities of the RCPC channel code desig[r] ...
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Joint design of vector quantizers and RCPC channel codes for Rayleigh fading channels
... The VQ used in the iterative de- sign is a Channel-Optimized Vector Quantizer (COVQ) that has been optimized for the index crossover proba- bilities of the RCPC channel code desig[r] ...
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