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Dissertations / Theses on the topic 'Nearest neighbors classifier'

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Published: 4 June 2021

Last updated: 12 February 2022

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1

Bernardina, Philipe Dalla. "PCA-tree: uma proposta para indexação multidimensional." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-29082007-114522/.

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Com o vislumbramento de aplicações que exigiam representações em espaços multidimensionais, surgiu a necessidade de desenvolvimento de métodos de acessos eficientes a estes dados representados em R^d. Dentre as aplicações precursoras dos métodos de acessos multidimensionais, podemos citar os sistemas de geoprocessamento, aplicativos 3D e simuladores. Posteriormente, os métodos de acessos multidimensionais também apresentaram-se como uma importante ferramenta no projeto de classificadores, principalmente classificadores pelos vizinhos mais próximos. Com isso, expandiu-se o espaço de representaç

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Gupta, Nidhi. "Mutual k Nearest Neighbor based Classifier." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

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Bermejo, Sánchez Sergio. "Learning with nearest neighbour classifiers." Doctoral thesis, Universitat Politècnica de Catalunya, 2000. http://hdl.handle.net/10803/6323.

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Premi extraordinari ex-aequo en l'àmbit d'Electrònica i Telecomunicacions. Convocatoria 1999 - 2000<br>Nearest Neighbour (NN) classifiers are one of the most celebrated algorithms in machine learning. In recent years, interest in these methods has flourished again in several fields (including statistics, machine learning and pattern recognition) since, in spite of their simplicity, they reveal as powerful non-parametric classification systems in real-world problems. The present work is mainly devoted to the development of new learning algorithms for these classifiers and is focused on the foll

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Reeder, John. "Hilbert Space Filling Curve (HSFC) Nearest Neighbor Classifier." Honors in the Major Thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/794.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering

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Kumar, Raja [Verfasser]. "Reducing the Computational Requirements of the Nearest Neighbor Classifier / Raja Kumar." München : GRIN Verlag, 2019. http://d-nb.info/1193490804/34.

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LOPES, Marcus Vinicius de Sousa. "Aplicação de classificadores para determinação de conformidade de biodiesel." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1896.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-04T17:47:07Z No. of bitstreams: 1 MarcusLopes.pdf: 2085041 bytes, checksum: 14f6f9bbe0d5b050a23103874af8c783 (MD5)<br>Made available in DSpace on 2017-09-04T17:47:07Z (GMT). No. of bitstreams: 1 MarcusLopes.pdf: 2085041 bytes, checksum: 14f6f9bbe0d5b050a23103874af8c783 (MD5) Previous issue date: 2017-07-26<br>The growing demand for energy and the limitations of oil reserves have led to the search for renewable and sustainable energy sources to replace, even partially, fossil fuels. Biodiesel has become in last decades the ma

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Mestre, Ricardo Jorge Palheira. "Improvements on the KNN classifier." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10923.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the

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Hatko, Stan. "k-Nearest Neighbour Classification of Datasets with a Family of Distances." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33361.

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The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the k-NN classifier. In this thesis we investigate the use of alternative distances for the k-NN classifier. We start by introducing some background notions in statistical machine learning. We define the k-NN classifier and discuss Stone's theorem and the proof that k-NN is universally consistent on the normed space R^d. We then prove that k-NN is universally consistent if we take a sequence of

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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Neo, Toh Koon Charlie. "A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Dastile, Xolani Collen. "Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002807.

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The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. H

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Naram, Hari Prasad. "Classification of Dense Masses in Mammograms." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1528.

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This dissertation material provided in this work details the techniques that are developed to aid in the Classification of tumors, non-tumors, and dense masses in a Mammogram, certain characteristics such as texture in a mammographic image are used to identify the regions of interest as a part of classification. Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and d

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Amlathe, Prakhar. "Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7050.

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Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation. Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony beha

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Jiao, Lianmeng. "Classification of uncertain data in the framework of belief functions : nearest-neighbor-based and rule-based approaches." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2222/document.

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Dans de nombreux problèmes de classification, les données sont intrinsèquement incertaines. Les données d’apprentissage disponibles peuvent être imprécises, incomplètes, ou même peu fiables. En outre, des connaissances spécialisées partielles qui caractérisent le problème de classification peuvent également être disponibles. Ces différents types d’incertitude posent de grands défis pour la conception de classifieurs. La théorie des fonctions de croyance fournit un cadre rigoureux et élégant pour la représentation et la combinaison d’une grande variété d’informations incertaines. Dans cette thè

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gundam, madhuri, and Madhuri Gundam. "Automatic Classification of Fish in Underwater Video; Pattern Matching - Affine Invariance and Beyond." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/1976.

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Underwater video is used by marine biologists to observe, identify, and quantify living marine resources. Video sequences are typically analyzed manually, which is a time consuming and laborious process. Automating this process will significantly save time and cost. This work proposes a technique for automatic fish classification in underwater video. The steps involved are background subtracting, fish region tracking and classification using features. The background processing is used to separate moving objects from their surrounding environment. Tracking associates multiple views of the same

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Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

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This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.

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Duan, Haoyang. "Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31113.

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From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares t

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Pauw, Theo. "Assessment of SPOT 5 and ERS-2 OBIA for mapping wetlands." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71906.

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Thesis (MSc)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: This research considered the automated remote sensing-based classification of wetland extent within the Nuwejaars and Heuningnes River systems on the Agulhas Plain. The classification process was based on meaningful image objects created through image segmentation rather than on single pixels. An expert system classifier was compared to a nearest-neighbour supervised classifier, and one multispectral (SPOT 5) image (dry season) and two C-band, VV-polarisation synthetic aperture radar (SAR: ERS-2) images (dry and wet season) were

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Vávrová, Eva. "Automatická klasifikace spánkových fází z polysomnografických dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-256520.

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The thesis is focused on analysis of polysomnographic signals based on extraction of chosen parameters in time, frequency and time-frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. The classification is realized by artificial neural networks, k-NN classifier and linear discriminant analysis. The program with a graphical user interface was created using Matlab.

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Guňka, Jiří. "Adaptivní klient pro sociální síť Twitter." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237052.

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The goal of this term project is create user friendly client of Twitter. They may use methods of machine learning as naive bayes classifier to mentions new interests tweets. For visualissation this tweets will be use hyperbolic trees and some others methods.

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Maršánová, Lucie. "Analýza experimentálních EKG záznamů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221365.

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This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, t

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Konečný, Antonín. "Využití umělé inteligence v technické diagnostice." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443221.

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The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machi

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"Learning with nearest neighbour classifiers." Universitat Politècnica de Catalunya, 2000. http://www.tesisenxarxa.net/TDX-0408103-145004/.

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Skalak, David Bingham. "Prototype selection for composite nearest neighbor classifiers." 1997. https://scholarworks.umass.edu/dissertations/AAI9737585.

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Combining the predictions of a set of classifiers has been shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. Increased accuracy has been shown in a variety of real-world applications, ranging from protein sequence identification to determining the fat content of ground meat. Despite such individual successes, the answers are not known to fundamental questions about classifier combination, such as "Can classifiers from any given model class be combined to create a composite classifier with higher accuracy?" or "Is it possi

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Chiang, Hsin-Kuan, and 江信寬. "Designing the Nearest Neighbor Classifiers via the VQ Method." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/22392078713707735102.

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Shi, Yi-Xiang, and 施逸祥. "Fuzzy K-Nearest Neighbor Classifier to Predict Protein Solvent Accessibility." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/01118152104627730914.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>95<br>Proteins have been played an important role in a creature and the numbers of proteins and their structures have been increased with years. Since protein applications are more widely used, there will be a lot of problems to be solved. Using a position-specific scoring matrix (PSSM) generated from PSI-BLAST in this thesis, we develop the modified fuzzy k-nearest neighbor method to predict the protein relative solvent accessibility. By modifying the membership functions of the fuzzy k-nearest neighbor method by Sim et al. [31], has recently been applied to

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Ho, Hsin-Hua, and 何省華. "A Novel k-Nearest-Neighbor Classifier Based on Nonparametric Separability." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/00058050545022755215.

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碩士<br>國立臺中教育大學<br>教育測驗統計研究所<br>94<br>The k-nearest-neighbor (k-NN) classifier is a simple and appealing classifier. A k-nearest-neighbor classifier expects the class conditional probabilities to be locally constant and suffers form bias in high dimensions. In this paper, we first use separability based on adaptive NWFE to establish an effective metric for computing a new neighborhood. The modified neighborhood shrinks in the direction with high separability and extends further in the other direction, i.e., the modified neighborhood extends further in the direction parallel to the decision boun

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Liu, Soundy, and 劉國聲. "Designing an Optimal Nearest Neighbor Classifier Using an Intelligent Genetic Algorithm." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/54270574850798014534.

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碩士<br>逢甲大學<br>資訊工程學系<br>89<br>In this paper, an efficient intelligent genetic algorithm (IGA) is proposed for designing an optimal k-nearest neighbor rule (k-NNR) classifier which has a high classification accuracy, a small reference set and a small feature set. Intelligent genetic algorithm improves the conventional genetic algorithm using orthogonal experimental designs to search for an optimal solution to the problem of designing an optimal k-NNR classifier. The intelligent genetic algorithm uses a novel intelligent crossover based on orthogonal arrays (OAs). The chromosomes of the childre

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Liu, Yu-Te, and 劉昱德. "Discriminant Adaptive Nearest Neighbor and Tangent Distance Classifier on Handwritten Recognition Problems." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/34656719662126103044.

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碩士<br>東海大學<br>統計學系<br>93<br>With respect to pattern recognition problems, when there are priori knowledge on the patterns, we usually incorporate them into the classification method. A very common type of priori knowledge is the transformation invariance of image-data. Simard et. al. (1993, 2000) proposed a transformation distance called "Tangent Distance (TD)" which can make pattern recognition be efficient. The key idea is to construct a distance measure which is invariant with respect to some chosen transformations includes x-translation, y-translation, rotation, scaling, parallel hyperboli

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Chen, Nai-Wen, and 陳艿玟. "Feature Weighting for k-Nearest Neighbor Classifiers Using Differential Evolution Algorithms." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/3gfxz4.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>104<br>Since the industrial revolution, people seek to replace human workers with machines in terms of benefits in labor, time and cost savings etc. With the advances in hardware and software technology in the recent years, data collected in practice are becoming larger, fast-changing and more complex. Big Data, which contain large-scale and/or high-dimensional data, cause serious obstacles for people in data interpretation and applications. As a result, machine learning has been a popular research topic within many fields of study. Machine learning, which can itera

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Ta-Chin, Chin, and 勤大慶. "A flexible codeword expansion method for VQ trained nearest neighbor classifiers." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/67423967607302917863.

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Yi-Cheng, Lin, and 林怡成. "A flexible training sample selection method for VQ trained nearest neighbor classifiers." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/27783060845329646006.

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Yen, Zhao-Hong, and 顏肇鴻. "Design of Optimal Nearest Neighbor Classifiers Using an Intelligent Multi-Objective Evolutionary Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/99769536590969631313.

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碩士<br>逢甲大學<br>資訊工程所<br>92<br>The k-nearest neighbor rule (k-NNR) is commonly used in applications of classifiers and data mining and the related area due to its simplicity and effectiveness. Theoretically, the goal of designing an optimal k-NNR classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. Recently, some studies tackled the multi-objective function by using the weighted-sum approach. However, such approaches are often criticized on its robustness, because they are sensitive to the weight values, and the weight values ar

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Chou, Cheng-Tung, and 周正東. "The application of GMM and K-nearest neighbor classifier in the study of EEG recognition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/93kwem.

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碩士<br>國立交通大學<br>統計學研究所<br>105<br>In this thesis, we investigate the feasibility of using Electroencephalography（EEG） to recognize the brain response under different conditions. In signal recognition, the Gaussian mixture distribution is a widely-used model. In this study, we adopt the Gaussian mixture model（GMM） to fit the EEG data, and propose using the K-nearest neighbor method to classify the data. We collect 17 healthy individuals’ EEG under the condition "Imagine sporting" and "Imagine relaxing", respectively. Then we use the GMM to fit the data. Then, we adopt the K-nearest neighbor clas

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Pulabaigari, Viswanath. "Pattern Synthesis Techniques And Compact Data Representation Schemes For Efficient Nearest Neighbor Classification." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1560.

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Li, I.-Jing, and 李怡靜. "A Study of the Condensation and Dimensionality Reduction Techniques for K-Nearest Neighbor Classifier Using Self Organizing Map Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/62918668337968358951.

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博士<br>國立中興大學<br>資訊科學與工程學系<br>101<br>The k nearest neighbor (kNN) algorithm is one of the most popular classification techniques and bas been widely used in pattern recognition related fields. This method assigns the label to a query pattern according to the most frequent class of the k nearest neighbors in the training samples. When the dimension becomes higher, it yields slow computational time because it needs to compare all training samples. The self-organizing map (SOM) is a completive learning algorithm which has been used as a visualization tool for dimensionality reduction. The self-org

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(6642491), Jingzhao Dai. "SPARSE DISCRETE WAVELET DECOMPOSITION AND FILTER BANK TECHNIQUES FOR SPEECH RECOGNITION." Thesis, 2019.

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<p>Speech recognition is widely applied to translation from speech to related text, voice driven commands, human machine interface and so on [1]-[8]. It has been increasingly proliferated to Human’s lives in the modern age. To improve the accuracy of speech recognition, various algorithms such as artificial neural network, hidden Markov model and so on have been developed [1], [2].</p> <p>In this thesis work, the tasks of speech recognition with various classifiers are investigated. The classifiers employed include the support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF

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Sharma, Govind. "Sentiment-Driven Topic Analysis Of Song Lyrics." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2472.

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Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA)

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