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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
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Introduction to vector quantization and its applications for numerics* [PDF]
ESAIM: Proceedings and Surveys, 2015 We present an introductory survey to optimal vector quantization and its first applications to Numerical Probability and, to a lesser extent to Information Theory and Data Mining. Both theoretical results on the quantization rate of a
Pagès Gilles
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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
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Deteksi Penyakit Diabetes Retinopati Pada Retina Mata Berdasarkan Pengolahan Citra [PDF]
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2017 Diabetic Retinopathy is a disease that strikes the retina of the eye in patients who have diabetes mellitus. Medical examination against sufferers of Diabetic Retinopathy is done with observation directly by eye surgeons. In this case, eye retinal images
Adri Pramana Putra Putra +2 more
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VQQL. Applying vector quantization to reinforcement learning [PDF]
, 2000 Proceeding of: RoboCup-99: Robot Soccer World Cup III, July 27 to August 6, 1999, Stockholm, SwedenReinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the ...
Borrajo Millán, Daniel +1 more
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Improving learning vector quantization using data reduction
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of
Pande Nyoman Ariyuda Semadi +1 more
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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
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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
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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
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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
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