Results 1 to 10 of about 9,834 (275)
Generalized relevance learning vector quantization
Neural Networks, 2002 Hammer B, Villmann T. Generalized Relevance Learning Vector Quantization. Neural Networks.
Barbara Hammer, Thomas Villmann
exaly +6 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
Ordinal regression based on learning vector quantization [PDF]
Neural Networks, 2017 Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the
Fengzhen Tang, Peter Tino
exaly +3 more sources
MECO: Mixture-of-Expert Codebooks for Multiple Dense Prediction Tasks [PDF]
SensorsAutonomous systems operating in embedded environments require robust scene understanding under computational constraints. Multi-task learning offers a compact alternative to deploying multiple task-specific models by jointly solving dense prediction ...
Gyutae Hwang, Sang Jun Lee
doaj +2 more sources
In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications [PDF]
Advanced ScienceIn‐memory computing based on nonvolatile synaptic arrays with computing functions has significantly improved the computing energy efficiency of neural networks.
Zhen Yang +9 more
doaj +2 more sources
Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments
Entropy, 2023 In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a data
Alexander Engelsberger, Thomas Villmann
doaj +1 more source
NSVQ: Noise Substitution in Vector Quantization for Machine Learning
IEEE Access, 2022 Machine learning algorithms have been shown to be highly effective in solving optimization problems in a wide range of applications. Such algorithms typically use gradient descent with backpropagation and the chain rule.
Mohammad Hassan Vali, Tom Backstrom
doaj +1 more source
Recurrent Learning Vector Quantization [PDF]
, 2021 Learning Vector Quantization (LVQ) methods have been popular choices of classification models ever since its introduction by T. Kohonen in the 90s. These days, LVQ is combined with Deep Learning methods to provide powerful yet interpretable machine ...
Ravichandran, Jensun, Villmann, Thomas
core +1 more source
Invotek: Jurnal Inovasi Vokasional dan Teknologi, 2020 Penelitian ini bertujuan untuk memberikan rekomendasi dari hasil perbandingan antara metode jaringan syaraf tiruan menggunakan metode backpropagation dan learning vector quantization (LVQ) dalam melakukan pengenalan pola.
Yeka Hendriyani
doaj +1 more source
IEEE Access, 2021 In a real-world environment, there are several difficult obstacles to overcome in classification. Those obstacles are data overlapping and skewness of data distribution. Overlapping data occur when many data from different classes overlap with each other;
Wisnu Jatmiko +7 more
doaj +1 more source