Add to ORKGHofmann D, Hammer B. Sparse approximations for kernel learning vector quantization. In: ESANN. 2013
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine...
Abstract: New non-asymptotic uniform error bounds for approximating func-tions in reproducing kernel...
Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We conside...
Schleif F-M. Sparse Kernel Vector Quantization with Local Dependencies. In: Proceedings of IJCNN 20...
Hofmann D, Gisbrecht A, Hammer B. Efficient approximations of robust soft learning vector quantizati...
Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. NeuroC...
Hofmann D, Hammer B. Kernel Robust Soft Learning Vector Quantization. In: Mana N, Schwenker F, Trent...
Efficient learning with non-linear kernels is often based on extracting features from the data that ...
Cette thèse a pour objectif d’étudier et de valider expérimentalement les bénéfices, en terme de qua...
We examine the problem of approximating the mean of a set of vectors as a sparse linear combination ...
Abstract. The contribution describes our application to the ESANN'2013 Competition on Human Act...
Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro ...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
The increasing number of classification applications in large data sets demands that efficient class...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine...
Abstract: New non-asymptotic uniform error bounds for approximating func-tions in reproducing kernel...
Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We conside...
Schleif F-M. Sparse Kernel Vector Quantization with Local Dependencies. In: Proceedings of IJCNN 20...
Hofmann D, Gisbrecht A, Hammer B. Efficient approximations of robust soft learning vector quantizati...
Hammer B, Hofmann D, Schleif F-M, Zhu X. Learning vector quantization for (dis-)similarities. NeuroC...
Hofmann D, Hammer B. Kernel Robust Soft Learning Vector Quantization. In: Mana N, Schwenker F, Trent...
Efficient learning with non-linear kernels is often based on extracting features from the data that ...
Cette thèse a pour objectif d’étudier et de valider expérimentalement les bénéfices, en terme de qua...
We examine the problem of approximating the mean of a set of vectors as a sparse linear combination ...
Abstract. The contribution describes our application to the ESANN'2013 Competition on Human Act...
Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro ...
In this paper we present a necessary and sufficient condition for global optimality of unsupervised ...
The increasing number of classification applications in large data sets demands that efficient class...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine...
Abstract: New non-asymptotic uniform error bounds for approximating func-tions in reproducing kernel...
Kernel-based learning methods provide their solutions as expansions in terms of a kernel. We conside...