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Prototype-based models in machine learning
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DOI：10.1002/wcs.1378.png)
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摘要
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An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. (C) 2016 Wiley Periodicals, Inc.
Keyword:
ORGANIZING FEATURE MAPS
NEURAL-GAS
VECTOR QUANTIZATION
DATA VISUALIZATION
NETWORK
SOM
PRESERVATION
SIMILARITY
BATCH
GTM
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3.8
论文数:
613
被引数:
2.8K
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