A review of learning vector quantization classifiers

Nova, David; Estévez, P. A.

doi:10.1007/s00521-013-1535-3

Cited by 110 publications

(123 citation statements)

References 66 publications

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“…We use the learning technique proposed in [23] that has gained much attention recently in the context of big data and interpretable models due to its flexibility and intuitive classification scheme, see e. g. [33], [34], [35], [36], [37], [38], [39], [40], [41], [42]. Essentially, it offers an efficient way for prototypebased data classification.…”

Section: Learning Vector Quantizationmentioning

confidence: 99%

Combining offline and online classifiers for life-long learning

Fischer

Hammer

Wersing³

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-One of the greatest challenges of life-long learning architectures is how to efficiently and reliably cope with the stability-plasticity dilemma. We propose an extension of a flexible system combining a static offline classifier and an incremental online classifier that is well suited for life-long learning scenarios. The pre-trained offline classifier preserves ground knowledge that should be respected during training, while the online classifier enables learning of new or specific information encountered during use. The combination is realised by a dynamic classifier selection strategy based on confidences of both ingredients. We report exemplary results of this architecture for the case of learning vector quantization (LVQ) for several data sets, thereby including an extensive comparison to alternative state of the art algorithms for incremental learning such as incremental generalised LVQ and the support vector machine.

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Section: Learning Vector Quantizationmentioning

confidence: 99%

Combining offline and online classifiers for life-long learning

Fischer

Hammer

Wersing³

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…The output of the output layer neurons that are connected to the winning neuron is 1, whereas the other is 0; then, the output provides the pattern class of the current input sample. The class learned by the hidden layer becomes a subclass, and the class learned by the output layer becomes the target class [30]. The architecture of the LVQ neural network is shown in Figure 3.…”

Section: Lvq Neural Networkmentioning

confidence: 99%

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Yao

2017

Journal of Sensors

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Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models.

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“…One can distinguish at least two main branches of LVQ the margin optimizer and the probabilistic variants [68]. The basic schemes for both variants are explained in the following.…”

Section: Basic Lvq Variantsmentioning

confidence: 99%

“…GLVQ also belong to margin optimizer based on the hypothesis margin [23]. The hypothesis margin is related to the distance that the prototypes can be altered without changing the classification decision [68]. Therefore, GLVQ can be seen as an alternative to SVMs [34,35].…”

Section: Introductionmentioning

confidence: 99%

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Kaden¹,

Lange²,

Nebel³

et al. 2014

Foundations of Computing and Decision Sciences

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Abstract. Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

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A review of learning vector quantization classifiers

Cited by 110 publications

References 66 publications

Combining offline and online classifiers for life-long learning

Combining offline and online classifiers for life-long learning

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

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