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Journal articles on the topic 'Learning Vector Quantization'

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Published: 4 June 2021

Last updated: 26 July 2025

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1

Matera, Fabio. "Learning Vector Quantization Networks." Substance Use & Misuse 33, no. 2 (1998): 271–82. http://dx.doi.org/10.3109/10826089809115864.

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2

LI, Rui-Ping, and Masao MUKAIDONO. "Proportional Learning Vector Quantization." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1129–34. http://dx.doi.org/10.3156/jfuzzy.10.6_1129.

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3

YAN, HONG. "CONSTRAINED LEARNING VECTOR QUANTIZATION." International Journal of Neural Systems 05, no. 02 (1994): 143–52. http://dx.doi.org/10.1142/s0129065794000165.

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Kohonen’s learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization (CLVQ). In this method the updated coefficients in each iteration are accepted only if the recognition performance of the classifier after updating is not decreased for the training samples compared with that before updating, a constraint widely

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4

Seo, Sambu, and Klaus Obermayer. "Soft Learning Vector Quantization." Neural Computation 15, no. 7 (2003): 1589–604. http://dx.doi.org/10.1162/089976603321891819.

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Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark

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5

Wu, Kuo-Lung, and Miin-Shen Yang. "Alternative learning vector quantization." Pattern Recognition 39, no. 3 (2006): 351–62. http://dx.doi.org/10.1016/j.patcog.2005.09.011.

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6

Orhan, Umut, and Enıs Arslan. "Learning Word-vector Quantization." ACM Transactions on Asian and Low-Resource Language Information Processing 19, no. 5 (2020): 1–18. http://dx.doi.org/10.1145/3397967.

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7

Wu, Xiao Hong, Bin Wu, and Jie Wen Zhao. "Noise Fuzzy Learning Vector Quantization." Key Engineering Materials 439-440 (June 2010): 367–71. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.367.

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Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates an

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8

Shigei, Noritaka, Hiromi Miyajima, and Michiharu Maeda. "Competitive Learning with Fast Neuron-Insertion." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 6 (2005): 590–98. http://dx.doi.org/10.20965/jaciii.2005.p0590.

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Adaptive Vector Quantization (AVQ) is to find a small set of weight vectors that well approximates a larger set of input vectors. This paper presents a fast AVQ method Competitive Learning with Approximate Neuron-Insertion (CLANI). Though neuron-insertion techniques can much enhance the accuracy in AVQ, a naive implementation requires a large computational cost proportional to the number of input vectors. Approximate neuron-insertion has an advantage that its computational cost is independent of the number of input vectors. We theoretically estimate the computational costs of CLANI and the oth

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9

Wu, Kuo Lung. "Unsupervised Kernel Learning Vector Quantization." Advanced Engineering Forum 6-7 (September 2012): 243–49. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.243.

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In this paper, we propose an unsupervised kernel learning vector quantization (UKLVQ) algorithm that combines the concepts of the kernel method and traditional unsupervised learning vector quantization (ULVQ). We first use the definition of the shadow kernel to give a general representation of the UKLVQ method and then easily implement the UKLVQ algorithm with a well-defined objective function in which traditional unsupervised learning vector quantization (ULVQ) becomes a special case of UKLVQ. We also analyze the robustness of our proposed learning algorithm by means of a sensitivity curve. I

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10

Sefta, Asfanji, and Syarif Hidayatulloh. "Verifikasi Citra Tanda Tangan Menggunakan Metode Prewitt dan Learning Vector Quantization." Jurnal Informatika 5, no. 2 (2018): 202–10. http://dx.doi.org/10.31311/ji.v5i2.3952.

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AbstrakTanda tangan adalah salah satu bukti persetujuan dari seseorang, Jadi tanda tangan ini memiliki arti yang sangat penting. Sering terjadi Kasus pemalsuan tanda tangan, antara lain disebabkan oleh sistem verifikasi yang tidak baik. Verifikasi tanda tangan ini kebanyakan dilakukan secara manual, Yaitu dengan membandingkan langsung dengan menggunakan mata Manusia yang memiliki banyak kelemahan. Jadi ketelitian dan keakuratan hasil yang diinginkan sering kurang memuaskan. Metode yang saya gunakan dalam membangun aplikasi verifikasi tanda tangan ini adalah dengan menggunakan metode Edge Detec

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11

Sefta, Asfanji, and Syarif Hidayatulloh. "Verifikasi Citra Tanda Tangan Menggunakan Metode Prewitt dan Learning Vector Quantization." Jurnal Informatika 5, no. 2 (2018): 202–10. http://dx.doi.org/10.31294/ji.v5i2.3952.

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AbstrakTanda tangan adalah salah satu bukti persetujuan dari seseorang, Jadi tanda tangan ini memiliki arti yang sangat penting. Sering terjadi Kasus pemalsuan tanda tangan, antara lain disebabkan oleh sistem verifikasi yang tidak baik. Verifikasi tanda tangan ini kebanyakan dilakukan secara manual, Yaitu dengan membandingkan langsung dengan menggunakan mata Manusia yang memiliki banyak kelemahan. Jadi ketelitian dan keakuratan hasil yang diinginkan sering kurang memuaskan. Metode yang saya gunakan dalam membangun aplikasi verifikasi tanda tangan ini adalah dengan menggunakan metode Edge Detec

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12

Villmann, Thomas, and Sven Haase. "Divergence-Based Vector Quantization." Neural Computation 23, no. 5 (2011): 1343–92. http://dx.doi.org/10.1162/neco_a_00110.

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Supervised and unsupervised vector quantization methods for classification and clustering traditionally use dissimilarities, frequently taken as Euclidean distances. In this article, we investigate the applicability of divergences instead, focusing on online learning. We deduce the mathematical fundamentals for its utilization in gradient-based online vector quantization algorithms. It bears on the generalized derivatives of the divergences known as Fréchet derivatives in functional analysis, which reduces in finite-dimensional problems to partial derivatives in a natural way. We demonstrate t

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13

Hammer, Barbara, and Thomas Villmann. "Generalized relevance learning vector quantization." Neural Networks 15, no. 8-9 (2002): 1059–68. http://dx.doi.org/10.1016/s0893-6080(02)00079-5.

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14

Salomón, Luis A., Jean-Claude Fort, and Li-Vang Lozada-Chang. "Average Competitive Learning Vector Quantization." Communications in Statistics - Simulation and Computation 43, no. 6 (2013): 1288–303. http://dx.doi.org/10.1080/03610918.2012.733469.

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15

Mavridis, Christos N., and John S. Baras. "Convergence of Stochastic Vector Quantization and Learning Vector Quantization with Bregman Divergences." IFAC-PapersOnLine 53, no. 2 (2020): 2214–19. http://dx.doi.org/10.1016/j.ifacol.2020.12.006.

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16

Yulian, Sulistia Rauf, and Suhartono Suhartono. "Pengenalan Bahasa Isyarat Huruf Abjad Menggunakan Metode Learning Vector Quantization (LVQ)." JURNAL MASYARAKAT INFORMATIKA 8, no. 1 (2017): 1–8. http://dx.doi.org/10.14710/jmasif.8.1.31450.

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Komunikasi paling efektif bagi mereka yang kurang beruntung (dalam hal ini penderita tuna rungu) adalah komunikasi non verbal. Komunikasi non verbal menggunakan gerakan tangan maupun gerakan tubuh dalam komunikasinya. Pada masyarakat umum masih sedikit yang mengerti bahasa isyarat, maka penelitian ini bertujuan mengimplementasikan aplikasi pengenalan bahasa isyarat huruf abjad secara komputasi menggunakan pengenalan pola. Jaringan syaraf tiruan Learning Vector Quantization (LVQ) dapat digunakan untuk melakukan klasifikasi sebuah pola berdasarkan permasalahan tertentu seperti halnya dalam penge

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17

C K, Arshitha. "ECG Classification Using Learning Vector Quantization." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (2018): 2699–702. http://dx.doi.org/10.22214/ijraset.2018.3599.

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18

Lau, H. Y. K., K. L. Mak, and I. S. K. Lee. "ADAPTIVE VECTOR QUANTIZATION FOR REINFORCEMENT LEARNING." IFAC Proceedings Volumes 35, no. 1 (2002): 493–98. http://dx.doi.org/10.3182/20020721-6-es-1901.01068.

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19

Karayiannis, N. B., and Pin-I Pai. "Fuzzy algorithms for learning vector quantization." IEEE Transactions on Neural Networks 7, no. 5 (1996): 1196–211. http://dx.doi.org/10.1109/72.536314.

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20

Wu, Kuo-Lung, and Miin-Shen Yang. "A fuzzy-soft learning vector quantization." Neurocomputing 55, no. 3-4 (2003): 681–97. http://dx.doi.org/10.1016/s0925-2312(02)00634-3.

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21

Brinkrolf, Johannes, Christina Göpfert, and Barbara Hammer. "Differential privacy for learning vector quantization." Neurocomputing 342 (May 2019): 125–36. http://dx.doi.org/10.1016/j.neucom.2018.11.095.

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22

Hammer, Barbara, Daniela Hofmann, Frank-Michael Schleif, and Xibin Zhu. "Learning vector quantization for (dis-)similarities." Neurocomputing 131 (May 2014): 43–51. http://dx.doi.org/10.1016/j.neucom.2013.05.054.

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23

Ahalt, Stanley C., Ashok K. Krishnamurthy, Prakoon Chen, and Douglas E. Melton. "Competitive learning algorithms for vector quantization." Neural Networks 3, no. 3 (1990): 277–90. http://dx.doi.org/10.1016/0893-6080(90)90071-r.

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24

Schneider, Petra, Michael Biehl, and Barbara Hammer. "Distance Learning in Discriminative Vector Quantization." Neural Computation 21, no. 10 (2009): 2942–69. http://dx.doi.org/10.1162/neco.2009.10-08-892.

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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. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ (GLVQ) and matrix learning in GLVQ. In these approaches, metric parameters are learned based on the

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25

Shen, Yuan-Yuan, Yan-Ming Zhang, Xu-Yao Zhang, and Cheng-Lin Liu. "Online semi-supervised learning with learning vector quantization." Neurocomputing 399 (July 2020): 467–78. http://dx.doi.org/10.1016/j.neucom.2020.03.025.

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26

Kästner, Marika, Barbara Hammer, Michael Biehl, and Thomas Villmann. "Functional relevance learning in generalized learning vector quantization." Neurocomputing 90 (August 2012): 85–95. http://dx.doi.org/10.1016/j.neucom.2011.11.029.

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27

Semadi, Pande Nyoman Ariyuda, and Reza Pulungan. "Improving learning vector quantization using data reduction." International Journal of Advances in Intelligent Informatics 5, no. 3 (2019): 218. http://dx.doi.org/10.26555/ijain.v5i3.330.

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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 data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can

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28

Hendriyani, Yeka. "Perbandingan Algoritma Backpropagation Dan Learning Vector Quantization (LVQ) dalam Pengenalan Pola Bangun Ruang Geometri." INVOTEK: Jurnal Inovasi Vokasional dan Teknologi 20, no. 2 (2020): 59–66. http://dx.doi.org/10.24036/invotek.v20i2.746.

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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. Kedua metode ini sering digunakan untuk aplikasi pengenalan pola, karena kedua metode ini mampu mengelompokkan pola-pola ke dalam kelas-kelas pola dan termasuk kedalam metode pembelajaran terawasi (supervised learning). Dalam penelitian ini akan dibuktikan metode backpropagation dan LVQ mampu mengenali pola bentuk geometri bangun datar serta menunjukkan metode mana yang lebih

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29

Hayat, Cynthia, and Iwan Aang Soenandi. "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 10, no. 1 (2024): 1–12. http://dx.doi.org/10.20473/jisebi.10.1.1-12.

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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. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quanti

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30

Engelsberger, Alexander, and Thomas Villmann. "Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments." Entropy 25, no. 3 (2023): 540. http://dx.doi.org/10.3390/e25030540.

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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 or a class distribution using a small set of prototypes, and hence, it belongs to interpretable models in machine learning. Further, the low complexity of vector quantizers makes them interesting for the application of quantum concepts for their implementation. This is especially true for current and upcoming generations of quantum devices, wh

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31

Wu, Bin, Xiang Li, Sheng Wei Qiu, Xiao Hong Wu, and Min Li. "Classification of Apple Varieties Using FT-NIR Spectroscopy and Possibilistic Learning Vector Quantization." Applied Mechanics and Materials 644-650 (September 2014): 1405–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1405.

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Red Fuji, Huaniu and Gala were classified by Fourier transform near infrared (FT-NIR) spectroscopy and possibilistic learning vector quantization (PLVQ) which was proposed to solve the noise sensitivity problem of fuzzy learning vector quantization (PLVQ). Firstly, apple NIR spectra were measured by FT-NIR spectrophotometer. Secondly, principal component analysis (PCA) was used to compress the dimensionality of NIR spectra which was high dimensional. Thirdly, fuzzy c-means (FCM) clustering was run to termination to obtain the cluster vectors for PLVQ. Finally, PLVQ was performed to classify th

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32

Zhang, Peng, Momoyo Ito, Shin-ichi Ito, and Minoru Fukumi. "Development of Eye Mouse Using EOG signals and Learning Vector Quantization Method." Journal of the Institute of Industrial Applications Engineers 3, no. 2 (2015): 52–58. http://dx.doi.org/10.12792/jiiae.3.52.

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33

Saeedi, Ehsan, Yinan Kong, and Md Selim Hossain. "Side-channel attacks and learning-vector quantization." Frontiers of Information Technology & Electronic Engineering 18, no. 4 (2017): 511–18. http://dx.doi.org/10.1631/fitee.1500460.

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34

Yadav, Priyanka, and Vineet Khanna. "Image Contrast Enhancement using Learning Vector Quantization." International Journal of Computer Applications 181, no. 20 (2018): 29–35. http://dx.doi.org/10.5120/ijca2018917911.

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35

Arif, M. Nurul, Misbah Misbah, and Yoedo Ageng Surya. "Klasifikasi Aroma Tembakau Menggunakan Learning Vector Quantization." E-Link : Jurnal Teknik Elektro dan Informatika 14, no. 2 (2020): 43. http://dx.doi.org/10.30587/e-link.v14i2.1198.

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Aroma tembakau ditentukan oleh kandungan gas-gas atau jumlah campuran bahan organik yang mudah menguap dan tidak mudah menguap. Proses penentuan sebelumnya telah di lakukan dengan metode analistis konvensional,yang melibatkan kombinasi antara manusia dan instrumentasi sekala besar. Metode ini sangat mahal dalam kaitannya dengan waktu dan tenaga kerja, karena membutuhkan peralatan yang sangat komplek dan tingkat ketelitian dari analisa yang di lakukan oleh ahli tembakau pada saat tertentu, karena indra penciuman ahli tembakau menjadi sangat rendah pada saat tertentu. karena indra penciuman manu

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36

Andriani, Siska, and Kotim Subandi. "Weather Forecast using Learning Vector Quantization Methods." Procedia of Social Sciences and Humanities 1 (March 2, 2021): 69–74. http://dx.doi.org/10.21070/pssh.v1i.22.

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Weather forecasting is one of the important factors in daily life, as it can affect the activities carried out by the community. The study was conducted to optimize weather forecasts using artificial neural network methods. The artificial neural network used is a learning vector quantization (LVQ) method, in which artificial neural networks based on previous research are suitable for prediction. The research is modeling weather forecast optimization using the LVQ method. Models with the best accuracy can be used in terms of weather forecasts. Based on the results of the training that has been

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37

Hofmann, T., and J. M. Buhmann. "Competitive learning algorithms for robust vector quantization." IEEE Transactions on Signal Processing 46, no. 6 (1998): 1665–75. http://dx.doi.org/10.1109/78.678486.

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38

Hariri, Fajar Rohman, Ema Utami, and Armadyah Amborowati. "Learning Vector Quantization untuk Klasifikasi Abstrak Tesis." Creative Information Technology Journal 2, no. 2 (2015): 128. http://dx.doi.org/10.24076/citec.2015v2i2.43.

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Data berukuran besar yang sudah disimpan jarang digunakan secara optimal karena manusia seringkali tidak memiliki waktu dan kemampuan yang cukup untuk mengelolanya. Data bervolume besar seperti data teks, jauh melampaui kapasitas pengolahan manusia yang sangat terbatas. Kasus yang disoroti adalah data abstrak tugas akhir mahasiswa jurusan teknik informatika Universitas Trunojoyo Madura. Dokumen tugas akhir oleh mahasiswa terkait hanya diupload pada SIMTAK (Sistem Informasi Tugas Akhir) dan pelabelan bidang minat penelitian dilakukan manual oleh mahasiswa tersebut, sehingga akan ada kemungkian

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39

Pedreira, C. E. "Learning vector quantization with training data selection." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 1 (2006): 157–62. http://dx.doi.org/10.1109/tpami.2006.14.

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40

Zhang, Yan-Xia, and Yong-Heng Zhao. "Learning Vector Quantization for Classifying Astronomical Objects." Chinese Journal of Astronomy and Astrophysics 3, no. 2 (2003): 183–90. http://dx.doi.org/10.1088/1009-9271/3/2/183.

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41

Bacciu, Davide, and Antonina Starita. "Expansive competitive learning for kernel vector quantization." Pattern Recognition Letters 30, no. 6 (2009): 641–51. http://dx.doi.org/10.1016/j.patrec.2009.01.002.

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42

Camastra, Francesco, and Alessandro Vinciarelli. "Cursive character recognition by learning vector quantization." Pattern Recognition Letters 22, no. 6-7 (2001): 625–29. http://dx.doi.org/10.1016/s0167-8655(01)00008-3.

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43

Tang, Fengzhen, and Peter Tiňo. "Ordinal regression based on learning vector quantization." Neural Networks 93 (September 2017): 76–88. http://dx.doi.org/10.1016/j.neunet.2017.05.006.

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44

Mwebaze, E., P. Schneider, F. M. Schleif, et al. "Divergence-based classification in learning vector quantization." Neurocomputing 74, no. 9 (2011): 1429–35. http://dx.doi.org/10.1016/j.neucom.2010.10.016.

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Villmann, Thomas, Sven Haase, and Marika Kaden. "Kernelized vector quantization in gradient-descent learning." Neurocomputing 147 (January 2015): 83–95. http://dx.doi.org/10.1016/j.neucom.2013.11.048.

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46

Odorico, Roberto. "Learning Vector Quantization with Training Count (LVQTC)." Neural Networks 10, no. 6 (1997): 1083–88. http://dx.doi.org/10.1016/s0893-6080(97)00012-9.

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47

Bezdek, James C., and Nikhil R. Pal. "Two soft relatives of learning vector quantization." Neural Networks 8, no. 5 (1995): 729–43. http://dx.doi.org/10.1016/0893-6080(95)00024-t.

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48

Wulanningrum, Resty, and Bagus Fadzerie Robby. "Learning Vector Quantization Image for Identification Adenium." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (2016): 383. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp383-389.

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Information and technology are two things that can not be separated and it has become a necessity for human life. Technology development at this time was not only used for intelligence purposes only, but has penetrated the world of holtikurtura. Adenium is one of the plants are much favored by ornamental plants lovers. Many of cultivation adenium who crosses that appear new varieties that have the color and shape are similar to each other. From this case, then made an application that can identify the type of adenium based on the image of that flower. Learning Vector quantization is one of the

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49

Schneider, Petra, Michael Biehl, and Barbara Hammer. "Adaptive Relevance Matrices in Learning Vector Quantization." Neural Computation 21, no. 12 (2009): 3532–61. http://dx.doi.org/10.1162/neco.2009.11-08-908.

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We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure o

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50

Villmann, T., M. Kaden, W. Hermann, and M. Biehl. "Learning vector quantization classifiers for ROC-optimization." Computational Statistics 33, no. 3 (2016): 1173–94. http://dx.doi.org/10.1007/s00180-016-0678-y.

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