Дисертації з теми "080109 Pattern Recognition and Data Mining"
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Seevinck, Jennifer. "Emergence in interactive art." Thesis, University of Technology, Sydney, 2011.
Знайти повний текст джерелаNguyen, Thuy Thi Thu. "Predicting cardiovascular risks using pattern recognition and data mining." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:3051.
Повний текст джерелаKou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.
Повний текст джерелаPh. D.
Gawande, Rashmi. "Evaluation of Automotive Data mining and Pattern Recognition Techniques for Bug Analysis." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-196770.
Повний текст джерелаLiu, Guimei. "Supporting efficient and scalable frequent pattern mining /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20LIUG.
Повний текст джерелаWu, Jianfei. "Vector-Item Pattern Mining Algorithms and their Applications." Diss., North Dakota State University, 2011. https://hdl.handle.net/10365/28841.
Повний текст джерелаKe, Yiping. "Efficient correlated pattern discovery in databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20KE.
Повний текст джерелаLeighty, Brian David. "Data Mining for Induction of Adjacency Grammars and Application to Terrain Pattern Recognition." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/212.
Повний текст джерелаLoekito, Elsa. "Mining simple and complex patterns efficiently using binary decision diagrams /." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/4378.
Повний текст джерелаFreeman, Dane Fletcher. "A product family design methodology employing pattern recognition." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50267.
Повний текст джерелаRatnayake, Uditha. "Application of the recommendation architecture model for text mining /." Access via Murdoch University Digital Theses Project, 2003. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20040713.113844.
Повний текст джерелаTang, Fung Michael, and 鄧峰. "Sequence classification and melody tracks selection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B29742973.
Повний текст джерелаLaw, Hiu Chung. "Clustering, dimensionality reduction, and side information." Diss., Connect to online resource - MSU authorized users, 2006.
Знайти повний текст джерелаTitle from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 296-317). Also issued in print.
Tang, Fung Michael. "Sequence classification and melody tracks selection /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25017470.
Повний текст джерелаLyra, Risto Matti Juhani. "Topical subcategory structure in text classification." Thesis, University of Sussex, 2019. http://sro.sussex.ac.uk/id/eprint/81340/.
Повний текст джерелаMinnen, David. "Unsupervised discovery of activity primitives from multivariate sensor data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24623.
Повний текст джерелаCommittee Chair: Thad Starner; Committee Member: Aaron Bobick; Committee Member: Bernt Schiele; Committee Member: Charles Isbell; Committee Member: Irfan Essa
Jin, Ruoming. "New techniques for efficiently discovering frequent patterns." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121795612.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center
Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.
Повний текст джерелаSantos, Jamilson Bispo dos. "Pesquisa de similaridades em imagens mamográficas com base na extração de características." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-23052014-010946/.
Повний текст джерелаThis work presents a computational strategy to consolidate the training of residents radiologists through the classification of mammographic images by similarity, analyzing information from reports made by experienced physicians, obtaining the attributes extracted from medical images. For the discovery of patterns that characterize the similarity apply techniques of digital image processing and data mining in mammographic images. Pattern recognition aims to achieve the classification of certain sets of images in classes. The classification of mammographic is performed using Artificial Neural Networks, through the classifier Self-Organizing Map (SOM). This work uses the image retrieval (CBIR-Content- Based Image Retrieval), considering the similarity in relation to an image already selected for training. The images are classified according to similarity, analyzing attribute information extracted from the images and reports. The identification of similarity was obtained by feature extraction, using the technique of wavelet transform.
Pirttikangas, S. (Susanna). "Routine Learning: from Reactive to Proactive Environments." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514275659.
Повний текст джерелаGuzmán, Ponce Angelica. "Nuevos Algoritmos Basados en Grafos y Clustering para el Tratamiento de Complejidades de los Datos." Tesis de doctorado, Universidad Autónoma del Estado de México, 2021. http://hdl.handle.net/20.500.11799/110464.
Повний текст джерелаNowadays, knowledge extraction from data is an essential task for decisionmaking in many areas. However, the data sets commonly present some negative problems (complexities) that decrease the performance in the knowledge extraction process. The imbalanced distribution of data between classes and the presence of noise and/or class overlap are data intrinsic characteristics that frequently decrease the performance of the knowledge extraction because data are assumed to keep a uniform distribution and free from any other problem. All these issues have been studied in Pattern Recognition and Data Mining, because of their impact on the performance of the learning models. Thus this Ph.D. thesis addresses class imbalance, class overlap and/or noise through techniques that reduce and clean the most represented class. Among the solutions to handle with the class imbalance problem, new algorithms based on graphs are proposed. This idea arises from the fact that many real-world problems (network analysis, chemical models, remote sensing, among others) have been tackled by using graph-based strategies, in which the problem is transformed in terms of vertices and edges. Keeping this in mind, the proposals presented in this Ph.D. thesis consider the most represented class as as a complete graph in such a way that a representative subset of majority class instances is obtained through reduction criteria. Regarding the data sets with class imbalance and class overlap and/or noise, the proposals include the use of clustering algorithms as a cleaning strategy. It is well known that these algorithms are used to group instances according to similar characteristics; however, the proposal here presented makes use of their ability to detect noisy instances. By this, the application of a clustering algorithm is carried out before facing the class imbalance. As a further extension to the proposals presented in this Ph.D. thesis and due to the growing interest in Big Data problems, the last part of this report introduces a graph-based algorithm to handle class imbalance in large-scale data sets.
Becas nacionales del CONACYT
Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
Повний текст джерелаIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
Lopes, Kelly Marques de Oliveira 1982. "Modelos baseados em data mining para classificação multitemporal de culturas no Mato Grosso utilizando dados de NDVI/MODIS." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307578.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-23T13:40:19Z (GMT). No. of bitstreams: 1 Lopes_KellyMarquesdeOliveira_M.pdf: 10053877 bytes, checksum: 2126c76ce80f71b89ec947645274c384 (MD5) Previous issue date: 2013
Resumo: O desenvolvimento de estudos na área de geotecnologia e o aumento na capacidade de armazenar dados têm melhorado a exploração e os estudos de imagens de satélites obtidas através de sensores orbitais. O mapeamento da cobertura da terra, estimativas de produtividade de culturas e a previsão de safras são informações importantes para o agricultor e para o governo, pois essas informações são essenciais para subsidiar decisões relacionadas à produção, estimativas de compra e venda, e cálculos de importação e exportação. Uma das alternativas para analisar dados de uso e cobertura da terra, obtidos por meio de sensores, é o uso de técnicas de mineração de dados, uma vez que essas técnicas podem ser utilizadas para transformar dados e informações em conhecimentos que irão subsidiar decisões relativas ao planejamento agrícola. Neste trabalho, foram utilizados dados multitemporais sobre o índice de vegetação NDVI, derivados de imagens do sensor MODIS, para o monitoramento das culturas de algodão, soja e milho no estado do Mato Grosso, no período do ano-safra de 2008/2009. O conjunto de dados, fornecido pela Embrapa Informática Agropecuária, foi composto por 24 colunas e 728 linhas, onde as 23 primeiras colunas referem-se aos valores do NVDI, e a última, à cobertura do solo. A metodologia utilizada teve como base o modelo CRISP-DM (Cross Industry Standard Process for Data Mining). Modelos preditivos para classificar dados sobre essas culturas foram elaborados e avaliados por algoritmos de aprendizado de máquina, tais como árvores de decisão (J48 e PART), florestas aleatórias (Random Forest). A seleção de atributos melhorou os valores do índice Kappa e a acurácia dos modelos. Foram geradas regras de classificação para mapear as culturas estudadas (soja, milho e algodão). Os resultados revelaram que os algoritmos de aprendizado de máquina são promissores para o problema de classificação de cobertura do solo. Em particular o algoritmo J48, utilizado em conjunto com a seleção de atributos feito por meio de análise de componentes principais, destacou-se em relação ao demais pela simplicidade e pelos valores apresentados. Os resultados também evidenciaram a presença regiões de cultivo do algodão em outras áreas do estado, fora daquelas estudadas
Abstract: The development of studies in the field of geotechnology and increased ability to store data have improved the exploration and study of satellite images obtained by satellite sensors. The mapping of land cover, estimates of crop productivity and crop forecasting is important information for the farmer and for the government, because this information is essential to support decisions related to production, estimates of purchase and sale, import and calculations and export. An alternative use for data analysis and coverage will be obtained by means of sensors, is the use of data mining techniques since these techniques can be used to transform data and information on the knowledge that will support decisions on agricultural planning. In this work, we used data on the multitemporal vegetation index NDVI derived from MODIS images for monitoring crops of cotton, soybean and corn in the state of Mato Grosso, in the period of the crop year 2008/2009. The dataset supplied by Embrapa Agricultural Informatics, comprised 24 columns and 728 rows, where the 23 first columns refer to the values of NVDI, and the last, the soil cover. The methodology used was based on the model CRISP-DM (Cross Industry Standard Process for Data Mining). Predictive models to classify data on these cultures were prepared and analyzed by machine learning algorithms such as decision trees (J48 and PART), Random Forests (Random Forest). The feature selection improved the Kappa index values and accuracy of the models. Classification rules were generated to map the cultures studied (soy, corn and cotton). The results show that the machine learning algorithms are promising for the problem of classification of land cover. In particular, the J48 algorithm, used in conjunction with feature selection done by principal component analysis, stood out against the other by the simplicity and the values presented. The results also revealed the presence of regions of cotton cultivation in other areas of the state, out of those studied
Mestrado
Matematica Aplicada e Computacional
Mestra em Matemática Aplicada e Computacional
Agarwal, Virat. "Algorithm design on multicore processors for massive-data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34839.
Повний текст джерелаKang, James M. "A query engine of novelty in video streams /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/977.
Повний текст джерелаXu, Yaomin. "New Clustering and Feature Selection Procedures with Applications to Gene Microarray Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196144281.
Повний текст джерелаFabbri, Renato. "Topological stability and textual differentiation in human interaction networks: statistical analysis, visualization and linked data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092017-154706/.
Повний текст джерелаEste trabalho relata propriedades topológicas estáveis (ou invariantes) e diferenciação textual em redes de interação humana, com referências derivadas de listas públicas de e-mail. A atividade ao longo do tempo e a topologia foram observadas em instantâneos ao longo de uma linha do tempo e em diferentes escalas. A análise mostra que a atividade é praticamente a mesma para todas as redes em escalas temporais de segundos a meses. As componentes principais dos participantes no espaço das métricas topológicas mantêm-se praticamente inalteradas quando diferentes conjuntos de mensagens são considerados. A atividade dos participantes segue o esperado perfil livre de escala, produzindo, assim, as classes de vértices dos hubs, dos intermediários e dos periféricos em comparação com o modelo Erdös-Rényi. Os tamanhos relativos destes três setores são essencialmente os mesmos para todas as listas de e-mail e ao longo do tempo. Normalmente, 3-12% dos vértices são hubs, 15-45% são intermediários e 44-81% são vértices periféricos. Os textos de cada um destes setores são considerados muito diferentes através de uma adaptação dos testes de Kolmogorov-Smirnov. Estas propriedades são consistentes com a literatura e podem ser gerais para redes de interação humana, o que tem implicações importantes para o estabelecimento de uma tipologia dos participantes com base em critérios quantitativos. De modo a guiar e apoiar esta pesquisa, também desenvolvemos um método de visualização para redes dinâmicas através de animações. Para facilitar a verificação e passos seguintes nas análises, fornecemos uma representação em dados ligados dos dados relacionados aos nossos resultados.
Schwarz, Ivan. "Rozpoznávání aktivit z trajektorií pohybujících se objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236165.
Повний текст джерелаLi, Yunming. "Machine vision algorithms for mining equipment automation." Thesis, Queensland University of Technology, 2000.
Знайти повний текст джерелаSun, Hongliang, and University of Lethbridge Faculty of Arts and Science. "Implementation of a classification algorithm for institutional analysis." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2008, 2008. http://hdl.handle.net/10133/738.
Повний текст джерелаviii, 38 leaves ; 29 cm. --
Kuiaski, Diogo Rosa. "Segmentação de pele em imagens digitais para a detecção automática de conteúdo ofensivo." Universidade Tecnológica Federal do Paraná, 2010. http://repositorio.utfpr.edu.br/jspui/handle/1/1338.
Повний текст джерелаO presente trabalho tem como objetivo estudar meios de efetuar a detecção automática de conteúdo ofensivo (pornografia) em imagens digitais. Para tal estudou-se largamente segmentação de pixels de pele, espaços de cor e descritores de conteúdo. Esse trabalho tem um foco maior na segmentação de pele, pois é a etapa primordial nos trabalhos envolvendo detecção de conteúdo ofensivo. Testou-se quatro métodos de segmentação de pixels de pele e foi construído um banco de dados estruturado para o estudo de segmentação de pele, com meios de anotação de imagens para auxiliar na estruturação e no controle das características das imagens do banco. Com o auxílio das metainformações do banco de imagens, foram conduzidos estudos envolvendo as condições de iluminação e a segmentação de pele. Por fim, foi implementado um algoritmo de extração de características em sistemas de classificação pelo conteúdo de imagens (CBIR) para detecção de conteúdo ofensivo.
This work presents a study of suitable approaches for automatic detection of offensive content (pornography) in digital images. Extensive experiments were conducted for skin pixel segmentation, colour spaces and content descriptors. This work focus its efforts on skin pixel segmentation, since this segmentation is the pre-processing stage for almost every content-based offensive image classification methods in the literature. Four skin skin segmentation methods were tested in six colour spaces. Also, a structured image database was built to help improve studies in skin segmentation, with the possibility of adding meta-information to the images in the database, such as illumination conditions and camera standards. With the help of meta information from the image database, experimets involving illumination conditions and skin colour segmentation were also done. Finally, some feature extraction algorithms were implemented in order to apply content-based image retrieval (CBIR) algorithms to classify offensive images.
Shakeel, Mohammad Danish. "Land Cover Classification Using Linear Support Vector Machines." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1231812653.
Повний текст джерелаHammal, Mohamed Ali. "Contribution à la découverte de sous-groupes corrélés : Application à l’analyse des systèmes territoriaux et des réseaux alimentaires." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI024.
Повний текст джерелаBetter feeding cities in quantity and quality, especially large cities, is a major challenge, whose resolution requires a better understanding of the relationships between urban populations and their food. On the scale of urban food systems, we need to understand the availability of food resources crossed with the socio-economic profiles of the territories. But we lack tools and methods to systematically understand the relationships between consumption basins, supply and eating habits. The objective of this thesis is to contribute to the development of new IT tools to process temporal, heterogeneous and multi-sources data in order to identify and characterize behaviors specific to a geographic area. For this, we rely on the joint exploration of gradual patterns, to discover rank correlations, and subgroups in order to find contexts for which the correlations described by the gradual patterns are exceptionally strong compared to the remaining of the data. We propose an enumeration algorithm based on pruning properties with upper bounds, as well as another algorithm which samples the patterns according to the quality measure. These approaches are validated not only on benchmark datasets, but also through an empirical study of the formation of food deserts in the Lyon urban area
Macedo, Charles Mendes de. "Aplicação de algoritmos de agrupamento para descoberta de padrões de defeito em software JavaScript." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-29012019-152129/.
Повний текст джерелаApplications developed with JavaScript language are increasing every day, not only for client-side, but also for server-side and for mobile devices. In this context, the existence of tools to identify faults is fundamental in order to assist developers during the evolution of their applications. Most of these tools use a list of predened faults that are discovered from the observation of the programming best practices and developer intuition. To improve these tools, the automatic discovery of faults and code smells is important because it allows to identify which ones actually occur in practice and frequently. A tool that implements a semiautomatic strategy for discovering bug patterns by grouping the changes made during the project development is the BugAID. The objective of this work is to contribute to the BugAID tool, extending this tool with improvements in the extraction of characteristics to be used by the clustering algorithm. The extended module that extracts the characteristics is called BE+. Additionally, an evaluation of the clustering algorithms used for discovering fault patterns in JavaScript software is performed
Colla, Ernesto Coutinho. "Aplicação de modelos gráficos probabilísticos computacionais em economia." reponame:Repositório Institucional do FGV, 2009. http://hdl.handle.net/10438/4261.
Повний текст джерелаWe develop a probabilistic model using Machine Learning tools to classify the trend of the Brazilian country risk expressed EMBI+ (Emerging Markets Bond Index Plus). The main goal is verify if Machine Learning is useful to build economic models which could be used as reasoning tools under uncertainty. Specifically we use Bayesian Networks to perform pattern recognition in observed macroeconomics and financial data. The results are promising. We get the main expected theoretical relationship between country risk and economic variables, as well as international economic context and market expectations.
O objetivo deste trabalho é testar a aplicação de um modelo gráfico probabilístico, denominado genericamente de Redes Bayesianas, para desenvolver modelos computacionais que possam ser utilizados para auxiliar a compreensão de problemas e/ou na previsão de variáveis de natureza econômica. Com este propósito, escolheu-se um problema amplamente abordado na literatura e comparou-se os resultados teóricos e experimentais já consolidados com os obtidos utilizando a técnica proposta. Para tanto,foi construído um modelo para a classificação da tendência do 'risco país' para o Brasil a partir de uma base de dados composta por variáveis macroeconômicas e financeiras. Como medida do risco adotou-se o EMBI+ (Emerging Markets Bond Index Plus), por ser um indicador amplamente utilizado pelo mercado.
Skabar, Andrew Alojz. "Inductive learning techniques for mineral potential mapping." Thesis, Queensland University of Technology, 2001.
Знайти повний текст джерелаBergfors, Anund. "Using machine learning to identify the occurrence of changing air masses." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-357939.
Повний текст джерелаSun, Le. "Data stream mining in medical sensor-cloud." Thesis, 2016. https://vuir.vu.edu.au/31032/.
Повний текст джерела"Fast frequent pattern mining." 2003. http://library.cuhk.edu.hk/record=b5891575.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 57-60).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Frequent Pattern Mining --- p.1
Chapter 1.2 --- Biosequence Pattern Mining --- p.2
Chapter 1.3 --- Organization of the Thesis --- p.4
Chapter 2 --- PP-Mine: Fast Mining Frequent Patterns In-Memory --- p.5
Chapter 2.1 --- Background --- p.5
Chapter 2.2 --- The Overview --- p.6
Chapter 2.3 --- PP-tree Representations and Its Construction --- p.7
Chapter 2.4 --- PP-Mine --- p.8
Chapter 2.5 --- Discussions --- p.14
Chapter 2.6 --- Performance Study --- p.15
Chapter 3 --- Fast Biosequence Patterns Mining --- p.20
Chapter 3.1 --- Background --- p.21
Chapter 3.1.1 --- Differences in Biosequences --- p.21
Chapter 3.1.2 --- Mining Sequential Patterns --- p.22
Chapter 3.1.3 --- Mining Long Patterns --- p.23
Chapter 3.1.4 --- Related Works in Bioinformatics --- p.23
Chapter 3.2 --- The Overview --- p.24
Chapter 3.2.1 --- The Problem --- p.24
Chapter 3.2.2 --- The Overview of Our Approach --- p.25
Chapter 3.3 --- The Segment Phase --- p.26
Chapter 3.3.1 --- Finding Frequent Segments --- p.26
Chapter 3.3.2 --- The Index-based Querying --- p.27
Chapter 3.3.3 --- The Compression-based Querying --- p.30
Chapter 3.4 --- The Pattern Phase --- p.32
Chapter 3.4.1 --- The Pruning Strategies --- p.34
Chapter 3.4.2 --- The Querying Strategies --- p.37
Chapter 3.5 --- Experiment --- p.40
Chapter 3.5.1 --- Synthetic Data Sets --- p.40
Chapter 3.5.2 --- Biological Data Sets --- p.46
Chapter 4 --- Conclusion --- p.55
Bibliography --- p.60
Shie, Chang-Luen, and 謝昌倫. "Pattern Recognition of Wafer Bin Maps with Data Mining Techniques." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/48438397888618575147.
Повний текст джерела淡江大學
統計學系
91
The aim of this paper is to explore Data Mining techniques for classification of the patterns of Wafer Bin Maps. Six classification methods were discussed in the paper, including two Neural Network classification techniques, three Decision Tree methods, and one statistical classification. To compare the capability of these classification methods, Random samples of Wafer Bin Maps were generated from seven different patterns with various levels of random noises, in order to classify these WBMs and to compute the correct-classification rates of these methods. Our simulation shows that Closest Class Mean Classifier method (CCMC) is most suitable for classification of Wafer Bin Maps patterns.
"Approach for mining multiple dependence structure with pattern recognition applications." 2003. http://library.cuhk.edu.hk/record=b6073568.
Повний текст джерела"June 2003."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (p. 125-136).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
Bean, Kathryn Brenda. "Supervised and unsupervised machine learning for pattern recognition and time series prediction /." 2008. http://proquest.umi.com/pqdweb?did=1654492021&sid=3&Fmt=2&clientId=10361&RQT=309&VName=PQD.
Повний текст джерелаLin, Ying-Tsu, and 林英足. "Pattern Recognition of Wafer Bin Maps with Data Mining Techniques and Machine Vision Methods." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/86525883931360540288.
Повний текст джерела淡江大學
統計學系碩士班
94
The human view-based methods are traditionally used in semi-conductor industry to trace production errors with the disadvantages such as time-wasting and subjectiveness. To enhance the accuracy of detection and the product rate, machine vision methods and Data Mining Techniques are applied in this study to develop a wafer-map analysis system. A two-phase method is adopted in our study. During the first phase, the ability of identification of the erroneous judgment by Support Vector Machine based method will be discussed. In the second phase, neural networks models and decision tree methos are adopted. Random samples of one-dimension and two-dimension wafer bin maps were generated from sixteen patterns with various levels of random noises to compare identification accuracy. Our study shows that the adoption of Support vector machine analysis increases the accuracy of identification. In the second phase, we find that mulit-layer perceptron neural network models functions best. Also, when the wafer data is converted to spatial data representation, both Neural Networks Model and Decision Tree Analysis Model increase the accuracy of identification.
Chen, Yi-Rong, and 陳奕戎. "Pattern Recognition of Wafer Bin Maps with Data Mining Techniques and Spatial Statistic Methods." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/11626038245621905471.
Повний текст джерела淡江大學
統計學系
92
The aim of this paper is to explore data mining techniques for classification of the patterns of wafer bin maps. Several classification methods are discussed in the paper, including two neural network techniques, two decision tree methods, one statistical classification, spatial statistic, and discriminant Analysis. In addition, we also discuss spatial representation of wafer bin-map data. To compare the capability of these classification methods, random samples of wafer bin maps were generated from sixteen different patterns with various levels of random noises, in order to classify these WBMs and to compute the correct-classification rates of these methods. Our simulation shows that Closest Class Mean Classifier method (CCMC) is most suitable for classification of wafer bin maps patterns. Besides, with spatial data representation, the rates of the correct classification increase in decision tree and neural network methods.
(6639122), Jihwan Lee. "Exploring Node Attributes for Data Mining in Attributed Graphs." Thesis, 2019.
Знайти повний текст джерелаChen, Wei-Ju, and 陳薇如. "Automatic Similarity Matching of Defect Patterns in Wafer Bin Map using Data Mining and Pattern Recognition Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/y2pv6y.
Повний текст джерелаHsu, Hsiu-Wen, and 許琇雯. "Real-time Pattern Recognition of Control Charts Patterns in Autocorrelated Process by a Data Mining Based Approach." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/mnrg6u.
Повний текст джерела國立虎尾科技大學
工業工程與管理研究所
97
Statistical process control (SPC) is an important method for control process in industry. It can detect assignable cause during the process control which may occur and provide help to improve process and reduce unnecessary product cost. Hence, control chart is an important tool at statistical process control. Control charts can detect abnormal status during the process control which may occur at any time. Essentially, the judgement of the process states can be seen as a classification problem in artificial intelligence. Effectively recognizing control chart patterns (CCPs) is a critical issue in statistical process control, since unnatural CCPs indicate potential quality problems at an early stage, to avoid defects before they are produced. Recently, decision tree (DT) is generally used in classification pattern, and a lot of researches point out that DT have excellent performances. This study examines the feasibility of utilizing a data mining technique DT learning in on-line CCP recognition for process with various levels of autocorrelation. An empirical comparison using simulation indicates that the fast learning of the DT model gives the SPC user the potential for building an automated CCP recognition system that can not only be applied on-line but also be trained in real time. This feature could make the CCP recognition system more adaptable to a dynamic manufacturing scenario.
(10710258), Tianshuai Guan. "MACHINE LEARNING BASED IDS LOG ANALYSIS." Thesis, 2021.
Знайти повний текст джерелаWith the rapid development of information technology, network traffic is also increasing dramatically. However, many cyber-attack records are buried in this large amount of network trafficking. Therefore, many Intrusion Detection Systems (IDS) that can extract those malicious activities have been developed. Zeek is one of them, and due to its powerful functions and open-source environment, Zeek has been adapted by many organizations. Information Technology at Purdue (ITaP), which uses Zeek as their IDS, captures netflow logs for all the network activities in the whole campus area but has not delved into effective use of the information. This thesis examines ways to help increase the performance of anomaly detection. As a result, this project intends to combine basic database concepts with several different machine learning algorithms and compare the result from different combinations to better find potential attack activities in log files.
Haghtalab, Siavash. "An Unsupervised Consensus Control Chart Pattern Recognition Framework." Master's thesis, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6101.
Повний текст джерелаM.S.
Masters
Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering; Systems Engineering Track
Saad, A., E. Avineri, Keshav P. Dahal, M. Sarfraz, and R. Roy. "Soft Computing in Industrial Applications." 2007. http://hdl.handle.net/10454/2290.
Повний текст джерела