Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Soft learning vector quantization
Neural Computation
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Novel Kernel Prototype-Based Learning Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
IEEE Computer Graphics and Applications
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
On the information and representation of non-Euclidean pairwise data
Pattern Recognition
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
Editorial: Hybrid learning machines
Neurocomputing
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Adaptive relevance matrices in learning vector quantization
Neural Computation
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Topographic mapping of large dissimilarity data sets
Neural Computation
Patch processing for relational learning vector quantization
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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While state-of-the-art classifiers such as support vector machines offer efficient classification for kernel data, they suffer from two drawbacks: the underlying classifier acts as a black box which can hardly be inspected by humans, and non-positive definite Gram matrices require additional preprocessing steps to arrive at a valid kernel. In this approach, we extend prototype-based classification towards general dissimilarity data resulting in a technology which (i) can deal with dissimilarity data characterized by an arbitrary symmetric dissimilarity matrix, (ii) offers intuitive classification in terms of prototypical class representatives, and (iii) leads to state-of-the-art classification results.