Relevant bibliographies by topics / Learning Vector Quantization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Learning Vector Quantization'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Learning Vector Quantization"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Learning Vector Quantization"

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Jabbar, Hussain. "Color Segmentation using LVQ-Learning Vector Quantization." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5315.

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This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in betwe
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Hofmann, Daniela [Verfasser]. "Learning vector quantization for proximity data / Daniela Hofmann." Bielefeld : Universitätsbibliothek Bielefeld, 2016. http://d-nb.info/1096457148/34.

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Soflaei, Shahrbabak Masoumeh. "Aggregated Learning: An Information Theoretic Framework to Learning with Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41399.

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Deep learning techniques have achieved profound success in many challenging real-world applications, including image recognition, speech recognition, and machine translation. This success has increased the demand for developing deep neural networks and more effective learning approaches. The aim of this thesis is to consider the problem of learning a neural network classifier and to propose a novel approach to solve this problem under the Information Bottleneck (IB) principle. Based on the IB principle, we associate with the classification problem a representation learning problem, which we
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Silva, Filho Telmo de Menezes e. "Uma abordagem adaptativa de learning vector quantization para classificação de dados intervalares." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/11453.

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Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-09T14:01:45Z No. of bitstreams: 2 Dissertacao Telmo Filho_DEFINITIVA.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Made available in DSpace on 2015-03-09T14:01:45Z (GMT). No. of bitstreams: 2 Dissertacao Telmo Filho_DEFINITIVA.pdf: 781380 bytes, checksum: fb398deff6f8aa856428277eb3236020 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27<br>A Análise de Dados Simbólicos l
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Kaden, Marika. "Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-206413.

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This thesis deals with the integration of auxiliary data knowledge into machine learning methods especially prototype based classification models. The problem of classification is diverse and evaluation of the result by using only the accuracy is not adequate in many applications. Therefore, the classification tasks are analyzed more deeply. Possibilities to extend prototype based methods to integrate extra knowledge about the data or the classification goal is presented to obtain problem adequate models. One of the proposed extensions is Generalized Learning Vector Quantization for direct
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ARAÚJO, Flávia Roberta Barbosa de. "Inferência de polimorfismos de nucleotídeo único utilizando algoritmos baseados em Relevance Learning Vector Quantization." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24890.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-25T20:59:33Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Flávia Roberta Barbosa de Araújo.pdf: 2622290 bytes, checksum: c1614ba289657ed54f8b6d463f91bfca (MD5)<br>Made available in DSpace on 2018-06-25T20:59:33Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Flávia Roberta Barbosa de Araújo.pdf: 2622290 bytes, checksum: c1614ba289657ed54f8b6d463f91bfca (MD5) Previous issue date: 2017-02-21<br>FACEPE<br>Embora duas pe
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Ayala, Garrido Brenda Elizabeth, and Bustamante Felipe Acevedo. "Control de semáforos para emergencias del Cuerpo General de Bomberos Voluntarios del Perú usando redes neuronales." Bachelor's thesis, Universidad Ricardo Palma, 2015. http://cybertesis.urp.edu.pe/handle/urp/1281.

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La presente tesis, tuvo como objetivo mostrar una estrategia a través de redes neuronales, para los vehículos del Cuerpo General de Bomberos Voluntarios del Perú (CGBVP) durante una emergencia en el distrito de Surco, contribuyendo a la fluidez vehicular de las unidades en situaciones de emergencia. A nivel mundial se puede apreciar que se han desarrollado diferentes estrategias o sistemas que apoyan a las unidades de emergencia. El desarrollo del sistema propuesto consiste en preparar los semáforos con anticipación al paso de una unidad. Para ello se consideraron dos tipos de datos, ubicac
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Leisch, Friedrich. "Bagged clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1272/1/document.pdf.

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A new ensemble method for cluster analysis is introduced, which can be interpreted in two different ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random effects of the training set are reduced by repeatedly training on resampled sets (bootstrap samples). We discuss the algorithm both from a more theoretical and an applied point of view and demonstrate it on several data sets. (author's abstract)<br>Series: W
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Clayton, Arnshea. "The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/19.

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In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input
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Ramesh, Rohit. "Abnormality detection with deep learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118542/1/Rohit_Ramesh_Thesis.pdf.

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This thesis is a step forward in developing the scientific basis for abnormality detection of individuals in crowded environments by utilizing a deep learning method. Such applications for monitoring human behavior in crowds is useful for public safety and security purposes.
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Books on the topic "Learning Vector Quantization"

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Merényi, Erzsébet, Michael J. Mendenhall, and Patrick O'Driscoll, eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28518-4.

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Villmann, Thomas, Frank-Michael Schleif, Marika Kaden, and Mandy Lange, eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07695-9.

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Villmann, Thomas, Marika Kaden, Tina Geweniger, and Frank-Michael Schleif, eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67159-3.

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Faigl, Jan, Madalina Olteanu, and Jan Drchal, eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15444-7.

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Vellido, Alfredo, Karina Gibert, Cecilio Angulo, and José David Martín Guerrero, eds. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-19642-4.

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P, Dhawan Atam, and United States. National Aeronautics and Space Administration., eds. LVQ and backpropagation neural networks applied to NASA SSME data. National Aeronautics and Space Administration, 1993.

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Merényi, Erzsébet, Patrick O'Driscoll, and Michael J. Mendenhall. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8 2016. Springer, 2016.

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Merényi, Erzsébet, Patrick O'Driscoll, and Michael J. Mendenhall. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, ... in Intelligent Systems and Computing). Springer, 2016.

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Villmann, Thomas, Frank-Michael Schleif, Marika Kaden, and Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4 2014. Springer London, Limited, 2014.

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Guerrero, José David Martín, Alfredo Vellido, Karina Gibert, and Cecilio Angulo. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization: Proceedings of the 13th International Workshop, ... in Intelligent Systems and Computing). Springer, 2019.

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Book chapters on the topic "Learning Vector Quantization"

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Kohonen, Teuvo. "Learning Vector Quantization." In Self-Organizing Maps. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56927-2_6.

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Kohonen, Teuvo. "Learning Vector Quantization." In Self-Organizing Maps. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-97610-0_6.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Learning Vector Quantization." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_464.

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Kohonen, Teuvo. "Learning Vector Quantization." In Self-Organizing Maps. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-97966-8_6.

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Zhang, Daoqiang, Songcan Chen, and Zhi-Hua Zhou. "Fuzzy-Kernel Learning Vector Quantization." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_31.

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Linder, T. "Learning-Theoretic Methods in Vector Quantization." In Principles of Nonparametric Learning. Springer Vienna, 2002. http://dx.doi.org/10.1007/978-3-7091-2568-7_4.

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Bauckhage, C., R. Ramamurthy, and R. Sifa. "Hopfield Networks for Vector Quantization." In Artificial Neural Networks and Machine Learning – ICANN 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61616-8_16.

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Xu, Ye, Shen Furao, Osamu Hasegawa, and Jinxi Zhao. "An Online Incremental Learning Vector Quantization." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_112.

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Hofmann, Daniela, and Barbara Hammer. "Kernel Robust Soft Learning Vector Quantization." In Artificial Neural Networks in Pattern Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33212-8_2.

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Visa, Ari. "Stability Study of Learning Vector Quantization." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_62.

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Conference papers on the topic "Learning Vector Quantization"

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Jeong, Taehee. "4bit-Quantization in Vector-Embedding for RAG." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00156.

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Lyu, Guan-Huei, Bagus Aris Saputra, Stefano Rini, Chung-Hsuan Sun, and Shih-Chun Lin. "Low-Rate Universal Vector Quantization for Federated Learning." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615475.

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Qian, Shuangyi, Shuaicong Gong, Chunming Zhao, and Ming Jiang. "MADDNESS Detector for MIMO Systems with Learning Vector Quantization." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683314.

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Schubert, Ronny, and Thomas Villmann. "About Vector Quantization and its Privacy in Federated Learning." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-57.

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Chen, Hang, Zhe Zhang, Ziwei Chen, and Haowen Ge. "Multi-head Discretization improves Learning Ability in Vector Quantization." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929116.

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Kaden, Marika, Ronny Schubert, Tina Geweniger, Wieland Hermann, and Thomas Villmann. "Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-64.

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Luu, Tung M., Thanh Nguyen, Tee Joshua Tian Jin, Sungwoon Kim, and Chang D. Yoo. "Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802066.

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Davies, Thomas, Ronny Schubert, Mandy Lange-Geisler, Klaus Dohmen, and Thomas Villmann. "Towards Learning Vector Quantization in the Setting of Homomorphic Encryption." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-47.

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P, Uma, and Perumal S. "Hydrocephalus Classification From MRI Using Learning Vector Quantization-Based Factorization Machine Deep Learning." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10957365.

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Grbovic, Mihajlo, and Slobodan Vucetic. "Regression Learning Vector Quantization." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.145.

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Reports on the topic "Learning Vector Quantization"

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Baras, John S., and Subhrakanti Dey. Combined Compression and Classification with Learning Vector Quantization. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada438572.

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Gabe V. Garcia. Eddy Current Signature Classification of Steam Generator Tube Defects Using A Learning Vector Quantization Neural Network. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/836575.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st
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