Generalized relevance learning vector quantization

By Barbara Hammer, Thomas Villmann. Neural Networks, 2002

473 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8f109a486a743f5c788a5ad53e4c23e60c8b1f96

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Michael Biehl
2. Udo Seiffert
3. Andreas Backhaus
4. Sven Haase
5. Barbara Hammer
6. Peter Riegler
7. Nestor Caticha
8. Petra Schneider

In Media Technology

1. Michael Biehl
2. Udo Seiffert
3. Andreas Backhaus
4. Sven Haase
5. Barbara Hammer
6. Peter Riegler
7. Nestor Caticha
8. Petra Schneider

Citations in the corpus

• Quantitative Measurements of model interpretability for the analysis of spectral data (2013, methodology): “For machine learning, three different classification models offering a relevance profile were considered: the Radial Basis Function (RBF) Network with Relevance Learning [5], [6], Generalized Relevance Learning Vector Quantization (GRLVQ) [7] and Supervised Relevance Neural Gas (SRNG) [8].”
• Statistical Mechanics of On-line Learning (2009, methodology): “Relevance LVQ (RLVQ) and related schemes update these factors according to a heuristic or gradient based scheme in the course of training [46,47].”
• Adaptive Relevance Matrices in Learning Vector Quantization (2009, methodology): “At the same time, it allows for straightforward interpretation of the result because the relevance profile can directly be interpreted as the contribution of the dimensions to the classification [15].”
• Mathematical Aspects of Divergence Based Vector Quantization Using Fr´ echet-Derivatives (2009, methodology): “D ( ·p|| · ) = 2 (p )2 as it known from [ 19 ].”