Learning and Vector Quantization (LVQ) networks
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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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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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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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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Image Contrast Enhancement using Learning Vector Quantization
... competitive learning neural networks such as the Self-Organizing Map algorithm that is a similar algorithm for unsupervised learning with the addition of connections between the ...list. ...
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Classification of meningiomas using discriminant wavelet packets and learning vector quantization
... 3 Results and Discussion The experimental setup consisted of employing the Daubechies 4 tap filter for obtaining the wavelet packet transform upto four levels. The data for other kinds of meningiomas, namely, Angiomatous ...
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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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Spectral Regularization in Generalized Matrix Learning Vector Quantization
... relevance learning,” IEEE Trans- actions on Neural Networks, ...and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January ...
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Differentiable Kernels in Generalized Matrix Learning Vector Quantization
... R EFERENCES [1] N. Aronszajn. Theory of reproducing kernels. Transactions of the American Mathematical Society, 68:337–404, 1950. [2] A. Backhaus, F. Bollenbeck, and U. Seiffert. High-throughput quality control of coffee ...
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Fast Learning Algorithm for Fuzzy Inference Systems using Vector Quantization
... a learning method of radial basis function (RBF) networks using VQ and generalized inverse method (GIM) is much ...new learning method using VQ, GIM and ...the learning time to about one-tenth ...
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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, ...
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Learning vector quantization for proximity data
... result different classification accuracies, but also different mathematical guar- antees as concerns convergence of the learning rules and generalization ability of the classifier. Besides this general view, we ...
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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 generation ...
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Distance Learning in Discriminative Vector Quantization
... unsupervised vector quantization schemes such as k-means or the self-organizing map is to represent typical data regions be means of ...online learning for the limit learning rule of RSLVQ, ...
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Distance learning in discriminative vector quantization
... unsupervised vector quantization schemes such as k-means or the self-organizing map is to represent typical data regions be means of ...online learning for the limit learning rule of RSLVQ, ...
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Divergence-based classification in learning vector quantization
... Here, we explore an alternative class of distance measures which relates to approaches based on statistics or information theory. So-called divergences, for instance the most popular Kullback-Leibler divergence, quantify ...
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Hankel matrices for use in Learning Vector Quantization
... Here, I present two topics that need to work in future: • To make a feature vector from the set of Hanklets, we used Bag-of-Word model. But this model does not take into account the temporal order of words. To get ...
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Divergence-based classification in learning vector quantization
... hyperparameter learning in order to determine, for instance, the optimal g directly in the training process, see [25] for a similar problem in the context of Robust Soft LVQ ...
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Generalized functional relevance learning vector quantization
... Generalized learning vector quantization (GRLVQ) is a proto- type based classification algorithm with metric adaptation weighting each data dimensions according to their relevance for the ...
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