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Dissertations / Theses on the topic 'Learning Vector Quantization'

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Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

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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4

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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Filho, Luiz Soares de Andrade. "Projeto de classificadores de padrÃes baseados em protÃtipos usando evoluÃÃo diferencial." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14230.

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Nesta dissertaÃÃo Ã apresentada uma abordagem evolucionÃria para o projeto eciente de classificadores baseados em protÃtipos utilizando EvoluÃÃo Diferencial. Para esta finalidade foram reunidos conceitos presentes na famÃlia de redes neurais LVQ (Learning Vector Quantization, introduzida por Kohonen para classificaÃÃo supervisionada, juntamente com conceitos extraÃdos da tÃcnica de clusterizaÃÃo automÃtica proposta por Das et al. baseada na metaheurÃstica EvoluÃÃo Diferencial. A abordagem proposta visa determinar tanto o nÃmero Ãtimo de protÃtipos por classe, quanto as posiÃÃes correspondentes

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Pahkasalo, Carolina, and André Sollander. "Adaptive Energy Management Strategies for Series Hybrid Electric Wheel Loaders." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166284.

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An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predict

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13

Bayik, Tuba Makbule. "Automatic Target Recognition In Infrared Imagery." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605388/index.pdf.

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The task of automatically recognizing targets in IR imagery has a history of approximately 25 years of research and development. ATR is an application of pattern recognition and scene analysis in the field of defense industry and it is still one of the challenging problems. This thesis may be viewed as an exploratory study of ATR problem with encouraging recognition algorithms implemented in the area. The examined algorithms are among the solutions to the ATR problem, which are reported to have good performance in the literature. Throughout the study, PCA, subspace LDA, ICA, nearest mean class

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cruz, Magnus Alencar da. "AvaliaÃÃo de redes neurais competitivas em tarefas de quantizaÃÃo vetorial:um estudo comparativo." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2016.

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nÃo hÃ<br>Esta dissertaÃÃo tem como principal meta realizar um estudo comparativo do desempenho de algoritmos de redes neurais competitivas nÃo-supervisionadas em problemas de quantizaÃÃo vetorial (QV) e aplicaÃÃes correlatas, tais como anÃlise de agrupamentos (clustering) e compressÃo de imagens. A motivaÃÃo para tanto parte da percepÃÃo de que hÃ uma relativa escassez de estudos comparativos sistemÃticos entre algoritmos neurais e nÃo-neurais de anÃlise de agrupamentos na literatura especializada. Um total de sete algoritmos sÃo avaliados, a saber: algoritmo K -mÃdias e as redes WTA, FSCL, S

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Lundberg, Emil. "Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.

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Vector Quantization (VQ) is a classic optimization problem and a simple approach to pattern recognition. Applications include lossy data compression, clustering and speech and speaker recognition. Although VQ has largely been replaced by time-aware techniques like Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW) in some applications, such as speech and speaker recognition, VQ still retains some significance due to its much lower computational cost — especially for embedded systems. A recent study also demonstrates a multi-section VQ system which achieves performance rivaling that of

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Gupta, Piyush. "Learning Decentralized Goal-Based Vector Quantization." Thesis, 1996. https://etd.iisc.ac.in/handle/2005/1682.

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Gupta, Piyush. "Learning Decentralized Goal-Based Vector Quantization." Thesis, 1996. http://etd.iisc.ernet.in/handle/2005/1682.

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Liang, Han-Wen, and 梁漢文. "Preclassified competitive-learning algorithms for vector quantization." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/59776420137974893958.

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Sun, Shan Chen, and 孫善政. "A new clustering network for learning vector quantization." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/31341177477937454752.

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碩士<br>國立清華大學<br>電機工程研究所<br>83<br>In this thesis, A new clustering neural network for learning vector quantization is investigated. First, conven- tional learning vector quantization algorithms (LVQ) suffer from two drawbacks, i.e., dead units and clusters, as a re- sult of improper choices of initial weights. In order to solve these problems, a robust learning vector quantization algorithm (RLVQ) is proposed. It can generate and eliminate learning weights according to the req

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20

Wang, Jiang-Shan, and 王江山. "Improved Learning Vector Quantization for Mixed-Type Data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/77276310988050953362.

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碩士<br>國立雲林科技大學<br>資訊管理系碩士班<br>99<br>With the rapid growth of electronic business, each enterprise has a large amount of electronic data, such as information of customers, information of transactions, etc. most of the data which owned by companies nowadays includes categorical data and numeric data. Learning Vector Quantization (LVQ) is a classification technique which can deal with a large amount of data. It is suitable to serve enterprises for data exploration. Traditional LVQ can’t directly handle categorical data, it requires conversion. A typical conversion is 1-of-k. However, after the co

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Liao, Shi-Chiang, and 廖世強. "Competitive Learning Algorithm and Its Applications to Vector Quantization." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/11101518185524828524.

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碩士<br>中原大學<br>電機工程學系<br>87<br>In this thesis, we present two novel competitive learning (CL) algorithms for the design of vector quantizers (VQs). In the fist part of this thesis, a new CL algorithm with k-winners-take-all activation is presented. The k winning neurons for updating are those best matching the input vector in the wavelet domain with subsapace search. To further reduce the computational time of our algorithm, we employ partial distance search (PDS) for finding winning neurons. Simulation results show that, for design a VQ with a high vector dimension and/or large number of code-

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Hsieh, Fu-Yung, and 謝福元. "Learning Vector Quantization Compatible LDPC Code for LMDS Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/665ybq.

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碩士<br>國立中央大學<br>通訊工程研究所<br>97<br>Abstracts In every microwave transmission technology, LMDS can be said and receive the note purpose most. LMDS system is a two-way digital cellular system operating at 20-50 GHz. It offers the multiple services of the audio, video, multimedia and data transmission and can meet all kinds of users'' demands. However, it is to influence the important factor in LMDS system which is rain attenuation and inter-cell interference (ICI). Therefore, LMDS is affected the interference problem by rain attenuation and inter-c ell interference (ICI). Avoiding the band-width u

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Hsin-Li, Pan, and 潘信利. "Learning Vector Quantization Neural Networks to Medical Diagnosis Problems Research." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/38440289258863126419.

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碩士<br>樹德科技大學<br>資訊管理研究所<br>96<br>Hospital information management has been computerized gradually, and the medical databases are now quite popular in contrast with traditional storage methods. The traditional manual method is not applicable for a large number of information processing. Moreover, medical diagnosis can only rely on past experience of physicians and there are many diversified factors of disease. In this research, the aim is to provide forecast and classification technology by using Artificial Neural Network in order to support the doctors to improve diagnosis with high accuracy. I

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Chang, Chin-Huang, and 張金璜. "Learning Vector Quantization Neural Networks for LED Wafer Defect Inspection." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/47582348564411150125.

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碩士<br>國立雲林科技大學<br>資訊工程研究所<br>95<br>Automatic visual inspection of defects plays an important role in industrial manufacturing with the benefits of low-cost and high accuracy. In light-emitting diode (LED) manufacturing, each die on the LED wafer must be inspected to determine whether it has defects or not. Therefore, detection of defective regions is a significant issue to discuss. In this paper, a new approach for inspection of LED wafer defects using the Learning Vector Quantization (LVQ) neural network is presented. In the wafer image, each die image can be taken and each of the regions o

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Lin, Yu-Jie, and 林裕傑. "Image Compression Using Watershed Segmentation and Vector Quantization Learning Network." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/05819609296356359884.

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碩士<br>國立勤益科技大學<br>電子工程系<br>97<br>In recent years, the development of digital multi-media techniques has become more and more popular. Due to the characteristics of digital multi-media, including storing, processing, transmitting easily, so that the requirement of multi-media techniques has huge increases; that makes the lack of memory capacity and internet bandwidth relatively. In digital multi-media field, image data are extensively applied especially. For the objective of reducing time costs of transmission and capacity of storage, it is essential to proceed image compression processing. 　　T

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Hong, Chou Jeng, and 周建宏. "A Fuzzy Learning Vector Quantization Network for Power Transformer Fault Diagnosis." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/63697111047870078041.

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碩士<br>中原大學<br>電機工程學系<br>87<br>To keep the power system in normal-operation status, detection of the incipient faults of the equipment in the systems play an important role, especially for the power transformers which have a wide-ranged effects on the power supply. If the incipient fault of the power transformer cannot be detected earlier, it will lead to serious consequences due to the evolution of the fault. As a result, for the purpose of protecting the power transformers and reducing both customers’ loss and inconvenience arising from the faults of equipment, periodic examination of the tra

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WANG, YAN-SHIANG, and 王彥翔. "Self-Organizing Map and Learning Vector Quantization for Stream Flow Forecasting." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65625932938053090883.

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碩士<br>國立臺灣大學<br>生物環境系統工程學系暨研究所<br>91<br>Flood forecasting is crucial for the safety of reservoir operation systems. Building an accurate and effective streamflow forecasting model has always been a major goal of hydrologists in Taiwan. Various types of artificial neural networks (ANNs) have been used to construct the rainfall-runoff processes. The input layer of ANNs is usually established by using the several preceding hour data of the upstream rainfall and streamflow gauges. That makes the dimensions of input large and the model complex. For reducing the complexity of the ANNs models, cluste

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Yang, Jenn-Hwai, and 楊鎮槐. "A Fuzzy-Soft Learning Vector Quantization for Control Chart Pattern Recognition." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/77928073781331909828.

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碩士<br>中原大學<br>數學研究所<br>89<br>This paper presents a supervised competitive learning network approach, called a fuzzy-soft learning vector quantization, for control chart pattern recognition. Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control (SPC). Hence control chart pattern recognition becomes more important in SPC. In order to effectively detect the patterns for the six main types of control charts, Pham and Oztemel described a class of pattern recognizers for control charts based on the learning vector quantization (L

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Tsai, Hung-Yi, and 蔡宏益. "Apply Extended Learning Vector Quantization to Classify Mixed and Categorical Data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/74473407761807387293.

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碩士<br>國立雲林科技大學<br>資訊管理系碩士班<br>100<br>With rapid growth of information technology, most of corporations have collected a large amount of digital data, such as data regarding employees, customers and transactions, etc. Thus, mining useful patterns from the data becomes an important issue. Learning Vector Quantization (LVQ) is a prototype-based classification technique and can process a large volume of data within reasonable computation time. However, traditional LVQ process only numeric data due to the use of Euclidean distance but cannot directly handle categorical data which must be converted

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Tsai, Bi-O., and 蔡碧娥. "Applications of Learning Vector Quantization to Direct Load Control Curves Classification Systems." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/23929407347467494437.

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碩士<br>中原大學<br>電機工程研究所<br>92<br>Abstract Starting with time of use policies, Taiwan Power Company (TPC) has adopted various strategies of load management since 1979, including interruptible rates, seasonal rates, central air conditioning duty cycling control, ice storage central air conditioning systems, and paging system in central air conditional duty cycling control programs. Performance of various load management strategies had been satisfied, but in recent years the effectiveness seems saturated with little growth. However, with the power demand ever increasing and supply not easily expand

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Maw-Rong, Leou, and 柳茂榮. "Fast Competitive Learning Algorithm and Its Application to Variable-Rate Vector Quantization." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/55128898952458602385.

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碩士<br>中原大學<br>電機工程研究所<br>86<br>In this thesis, a novel competitive learning algorithm for the design ofvariable-rate vector quantizers (VQs) is presented. The algorithm, termedvariable-rate competitive learning (VRCL) algorithm, constructs a VQ havingminimum average distortion subject to a rate constraint. In addition, thealgorithm enjoys a better rate-distortion performance than that of otherexisting fixed-rate VQ design algorithms and conventional variable-rate ECVQalgorithm. Base on the structure of neural network, the algorithmis also insensitive to selection of initial codewords. For thes

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Huang, Jin-ruei, and 黃進瑞. "Combining latent semantic analysis and learning vector quantization to construct hybrid filtering recommender." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/55516339392716802687.

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碩士<br>國立雲林科技大學<br>資訊管理系碩士班<br>101<br>Content-based filtering and collaborative filtering are often used techniques in recommendation system. The former method analyzes product attribute from users by using similarity to make recommendation. The latter method analyzes rating records from users by using similarity to make recommendation. Since the number of users and products are increasing as time goes on, many studies tend to add more product attributes to analyze similarity. While ignoring the content description from products, the prediction errors will be increased. In recent years, some st

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Kaden, Marika. "Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models." Doctoral thesis, 2015. https://ul.qucosa.de/id/qucosa%3A14833.

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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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Zhuang, Da-Feng, and 莊達峰. "Using Fuzzy Algorithms for Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84706557999274911164.

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碩士<br>國立雲林科技大學<br>資訊工程研究所<br>93<br>The objective of the thesis is to develop a suite of effective and prompt methods for recurrent nasal papilloma (RNP) detection. The magnetic resonance image (MRI) is one of the common auxiliary tools utilized to clinically diagnose recurrent nasal papilloma nowadays. Owing to the response of RNP regions in Gadolinium-enhanced magnetic resonance images is different form the response of normal tissues, the difference between the dynamic-MR images before and after administering contrast material can be calculated and extract the suspicious RNP regions automati

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Chang, Ya-Ting, and 張雅婷. "Evaluating the process of a genetic algorithm for improving the learning vector quantization neural networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/09609339306956125114.

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碩士<br>樹德科技大學<br>資訊管理研究所<br>97<br>Artificial Intelligence（AI）has been applied to the research fields such as network management areas, expert systems, and machine learning. It has also been wildly used in the study of classification. The Learning Vector Quantization（LVQ）neural network, a classification method that use a competitive supervised learning algorithm, is one of the most promising approaches in the field of Artificial Neural Networks（ANN）. Genetic Algorithms（GA）have proven an ability to improve the classification performance of ANN by optimizing their topology and parameter settings.

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Hong, Yong-Cheng, and 洪永政. "Using Learning Vector Quantization Neural Network for Thyroid Segmentation and Volume Estimation in CT images." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/78dz84.

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碩士<br>國立雲林科技大學<br>資訊工程研究所<br>96<br>Thyroid is one of the most important endocrine of human, and thyroid diseases are common diseases for our national. Because thyroid volume is significant indicator for diagnosing diseases, it is greatly helpful for physicians to diagnose by measuring thyroid volumes. Computer tomography (CT) is a common Computer Aided Diagnosis (CAD) tool for clinical thyroid diseases diagnosis. Nevertheless, even if the thyroid regions can acquire with hand-marked from CT images. The procedure of outlining the thyroid regions by physicians consumes a good deal of time and mi

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Machireddy, Amrutha. "Learning Non-linear Mappings from Data with Applications to Priority-based Clustering, Prediction, and Detection." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5670.

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With the volume of data generated in today's internet-of-things, learning algorithms to extract and understand the underlying relations between the various attributes of data have gained momentum. This thesis is focused on learning algorithms to extract meaningful relations from the data using both unsupervised and supervised learning algorithms. Vector quantization techniques are popularly used for applications in contextual data clustering, data visualization and high-dimensional data exploration. Existing vector quantization techniques, such as, the K-means and its variants and those der

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Strickert, Marc. "Self-Organizing Neural Networks for Sequence Processing." Doctoral thesis, 2005. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2005012711.

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This work investigates the self-organizing representation of temporal data in prototype-based neural networks. Extensions of the supervised learning vector quantization (LVQ) and the unsupervised self-organizing map (SOM) are considered in detail. The principle of Hebbian learning through prototypes yields compact data models that can be easily interpreted by similarity reasoning. In order to obtain a robust prototype dynamic, LVQ is extended by neighborhood cooperation between neurons to prevent a strong dependence on the initial prototype locations. Additionally, implementations of more gene

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Aliyari, Ghassabeh Youness. "ON THE CONVERGENCE AND APPLICATIONS OF MEAN SHIFT TYPE ALGORITHMS." Thesis, 2013. http://hdl.handle.net/1974/8365.

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Mean shift (MS) and subspace constrained mean shift (SCMS) algorithms are non-parametric, iterative methods to find a representation of a high dimensional data set on a principal curve or surface embedded in a high dimensional space. The representation of high dimensional data on a principal curve or surface, the class of mean shift type algorithms and their properties, and applications of these algorithms are the main focus of this dissertation. Although MS and SCMS algorithms have been used in many applications, a rigorous study of their convergence is still missing. This dissertation aims

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