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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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Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks. [PDF]
Adv Sci (Weinh)Huang C +6 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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Distance Learning in Discriminative Vector Quantization [PDF]
Neural Computation, 2009 Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters.
Schneider, P. +2 more
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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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Prediksi Jenis Cacing Nematoda Usus Yang Menginfeksi Siswa Dengan Menggunakan Metoda LVQ
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 2017 Abstrak-Pada saat ini, Jaringan Syaraf Tiruan (JST) telah banyak menjadi objek penelitian yang menarik, karena penerapannya sangat potensial dalam berbagai bidang sains, salah satu penerapannya didalam memprediksi penyakit. Penelitian ini bertujuan untuk
Erni Rouza
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Identification of Formaldehyde Bananas using Learning Vector Quantization
Indonesian Journal of Information Systems, 2021 Bananas that ripen with chemical process or do not ripen naturally usually, this can be recognized by the presence of blackish patches on the surface of the skin.
Rahmat Musa, Mutaqin Akbar
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Jurnal Sarjana Teknik Informatika, 2019 Materi Learning vector quantization pada mata kuliah jaringan saraf tiruan bagi sebagian mahasiswa dirasa sulit untuk dipahami. Materi learning vector quantization dirasa sulit oleh mahasiswa pada soal penyelesaian langkah-langkah menentukan pengkodean ...
Cahyoko Hariya Pamilu, Ardi Pujiyanta
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