We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning
Learning vector quantization (LVQ) schemes constitute intuitive, powerful classification heuris-tics...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explici...
We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (K...
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M. Generalized derivative based Kernelized lea...
In the present paper we investigate the application of differentiable kernel for generalized matrix ...
We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization...
International audienceThe kernel trick is a well known approach allowing to implicitly cast a linear...
Supervised and unsupervised prototype based vector quantization frequently are pro-ceeded in the Euc...
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classi...
In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGen...
Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions...
We propose a new learning method, "Generalized Learning Vec-tor Quantization (GLVQ), " in ...
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. ...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
Learning vector quantization (LVQ) schemes constitute intuitive, powerful classification heuris-tics...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explici...
We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (K...
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M. Generalized derivative based Kernelized lea...
In the present paper we investigate the application of differentiable kernel for generalized matrix ...
We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization...
International audienceThe kernel trick is a well known approach allowing to implicitly cast a linear...
Supervised and unsupervised prototype based vector quantization frequently are pro-ceeded in the Euc...
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classi...
In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGen...
Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions...
We propose a new learning method, "Generalized Learning Vec-tor Quantization (GLVQ), " in ...
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. ...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
Learning vector quantization (LVQ) schemes constitute intuitive, powerful classification heuris-tics...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
This paper proposes a variant of the generalized learning vector quantizer (GLVQ) optimizing explici...