• Publications
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Generalized relevance learning vector quantization
tl;dr
We propose a scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. Expand
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  • Open Access
Adaptive Relevance Matrices in Learning Vector Quantization
tl;dr
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. Expand
  • 306
  • 30
  • Open Access
Merge SOM for temporal data
tl;dr
In this paper, we investigate its theoretical and practical properties. Expand
  • 122
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Supervised Neural Gas with General Similarity Measure
tl;dr
We propose a generalization of learning vector quantization with three additional features: (I) it directly integrates neighborhood cooperation, hence is less affected by local optima; (II) the method can be combined with any differentiable similarity measure whereby metric parameters such as relevance factors of the input dimensions can automatically be adapted according to the given data; (III) it obeys a gradient dynamics. Expand
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Topographic Mapping of Large Dissimilarity Data Sets
tl;dr
In this article, we introduce relational topographic maps as an extension of relational clustering algorithms, which offer prototype-based representations of dissimilarity data, to incorporate neighborhood structure. Expand
  • 104
  • 11
  • Open Access
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift
tl;dr
SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspiredmemory models and their coordination. Expand
  • 75
  • 11
  • Open Access
Relevance determination in Learning Vector Quantization
tl;dr
We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. Expand
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Incremental learning algorithms and applications
tl;dr
We formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications. Expand
  • 115
  • 9
  • Open Access
Distance Learning in Discriminative Vector Quantization
tl;dr
We introduce matrix learning to a recent statistical formalization of LVQ, robust soft LVQ and compare the results on several artificial and real-life data sets to matrix learning in GLVQ. Expand
  • 104
  • 8
  • Open Access
Incremental on-line learning: A review and comparison of state of the art algorithms
tl;dr
We analyze the key properties of eight popular incremental methods representing different algorithm classes and show how they perform in comparison to each other. Expand
  • 104
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