Fischer L, Nebel D, Villmann T, Hammer B, Wersing H. Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches. In: Villmann T, Schleif F-M, Kaden M, Lange M, eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing. Vol 295. Cham: Springer International Publishing; 2014: 109-118
Fischer L, Hammer B, Wersing H. Efficient rejection strategies for prototype-based classification. N...
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classi...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
Fischer L, Hammer B, Wersing H. Rejection strategies for learning vector quantization. In: Verleysen...
Fischer L, Hammer B, Wersing H. Local Rejection Strategies for Learning Vector Quantization. In: Wer...
Abstract. We present prototype-based classification schemes, e. g. learn-ing vector quantization, wi...
Abstract. In this contribution, we focus on reject options for prototype-based classifiers, and we p...
Artelt A, Brinkrolf J, Visser R, Hammer B. Explaining Reject Options of Learning Vector Quantization...
Biehl M, Ghosh A, Hammer B. Learning vector quantization: The dynamics of winner-takes-all algorithm...
The field of machine learning concerns the design of algorithms to learn and recognize complex patte...
Digital data is rising day by day and so is the need for intelligent, automated data processing in d...
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the frame...
Witoelar A, Biehl M, Hammer B. Learning Vector Quantization: generalization ability and dynamics of ...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
Due to intuitive training algorithms and model representation, prototype-based models are popular in...
Fischer L, Hammer B, Wersing H. Efficient rejection strategies for prototype-based classification. N...
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classi...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...
Fischer L, Hammer B, Wersing H. Rejection strategies for learning vector quantization. In: Verleysen...
Fischer L, Hammer B, Wersing H. Local Rejection Strategies for Learning Vector Quantization. In: Wer...
Abstract. We present prototype-based classification schemes, e. g. learn-ing vector quantization, wi...
Abstract. In this contribution, we focus on reject options for prototype-based classifiers, and we p...
Artelt A, Brinkrolf J, Visser R, Hammer B. Explaining Reject Options of Learning Vector Quantization...
Biehl M, Ghosh A, Hammer B. Learning vector quantization: The dynamics of winner-takes-all algorithm...
The field of machine learning concerns the design of algorithms to learn and recognize complex patte...
Digital data is rising day by day and so is the need for intelligent, automated data processing in d...
Winner-Takes-All (WTA) prescriptions for learning vector quantization (LVQ) are studied in the frame...
Witoelar A, Biehl M, Hammer B. Learning Vector Quantization: generalization ability and dynamics of ...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
Due to intuitive training algorithms and model representation, prototype-based models are popular in...
Fischer L, Hammer B, Wersing H. Efficient rejection strategies for prototype-based classification. N...
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classi...
Biehl M, Gosh A, Hammer B. The dynamics of Learning Vector Quantization. In: Verleysen M, ed. ESANN'...