Learning vector quantization - Open Access .click

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VQQL. Applying Vector Quantization to Reinforcement Learning [PDF]

, 2000
Publicado
Fernando Fernández, Daniel Borrajo
core +8 more sources

Self-incremental learning vector quantization with human cognitive biases [PDF]

Scientific Reports, 2021
Human beings have adaptively rational cognitive biases for efficiently acquiring concepts from small-sized datasets. With such inductive biases, humans can generalize concepts by learning a small number of samples. By incorporating human cognitive biases
Nobuhito Manome +4 more
doaj +2 more sources

Learning Vector Quantization with Training Count (LVQTC) [PDF]

Neural Networks, 1997
Kohonen's learning vector quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training, this allows for dynamic self-allocation of the neurons to classes. In the classification stage training counters provide an estimate of the reliability of classification of the single neurons ...
R. Odorico
openalex +5 more sources

Differential privacy for learning vector quantization [PDF]

Neurocomputing, 2019
Abstract Prototype-based machine learning methods such as learning vector quantisation (LVQ) offer flexible classification tools, which represent a classification in terms of typical prototypes. This representation leads to a particularly intuitive classification scheme, since prototypes can be inspected by a human partner in the same way as data ...
Johannes Brinkrolf, Christina Göpfert, Barbara Hammer +2 more
openalex +5 more sources

Divergence-based classification in learning vector quantization [PDF]

Neurocomputing, 2011
We discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study divergence based learning vector quantization (DLVQ). We derive cost
Ernest Mwebaze +7 more
openalex +5 more sources

Kernel Robust Soft Learning Vector Quantization [PDF]

, 2012
Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability of the results and scalability of the model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have the benefit of a solid mathematical foundation of the learning rule and ...
Daniela Hofmann, Barbara Hammer
openalex +4 more sources

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Journal of Information Systems Engineering and Business Intelligence
Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children.
Cynthia Hayat, Iwan Aang Soenandi
doaj +3 more sources

Round Randomized Learning Vector Quantization for Brain Tumor Imaging. [PDF]