Relevant bibliographies by topics / FEATURE OPTIMIZATION METHODS / Dissertations / Theses
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Dissertations / Theses on the topic 'FEATURE OPTIMIZATION METHODS'

Author: Grafiati
Published: 11 September 2023

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1

Lin, Lei. "Optimization methods for inventive design." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD012/document.

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La thèse traite des problèmes d'invention où les solutions des méthodes d'optimisation ne satisfont pas aux objectifs des problèmes à résoudre. Les problèmes ainsi définis exploitent, pour leur résolution, un modèle de problème étendant le modèle de la TRIZ classique sous une forme canonique appelée "système de contradictions généralisées". Cette recherche instrumente un processus de résolution basé sur la boucle simulation-optimisation-invention permettant d'utiliser à la fois des méthodes d'optimisation et d'invention. Plus précisément, elle modélise l'extraction des contractions généralisée
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2

Zanco, Philip. "Analysis of Optimization Methods in Multisteerable Filter Design." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2227.

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The purpose of this thesis is to study and investigate a practical and efficient implementation of corner orientation detection using multisteerable filters. First, practical theory involved in applying multisteerable filters for corner orientation estimation is presented. Methods to improve the efficiency with which multisteerable corner filters are applied to images are investigated and presented. Prior research in this area presented an optimization equation for determining the best match of corner orientations in images; however, little research has been done on optimization techniques to
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3

Monrousseau, Thomas. "Développement du système d'analyse des données recueillies par les capteurs et choix du groupement de capteurs optimal pour le suivi de la cuisson des aliments dans un four." Thesis, Toulouse, INSA, 2016. http://www.theses.fr/2016ISAT0054.

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Dans un monde où tous les appareils électro-ménagers se connectent et deviennent intelligents, il est apparu pour des industriels français le besoin de créer des fours de cuisson innovants capables de suivre l’état de cuisson à cœur de poissons et de viandes sans capteur au contact. Cette thèse se place dans ce contexte et se divise en deux grandes parties. La première est une phase de sélection d’attributs parmi un ensemble de mesures issues de capteurs spécifiques de laboratoire afin de permettre d’appliquer un algorithme de classification supervisée sur trois états de cuisson. Une méthode d
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4

Xiong, Xuehan. "Supervised Descent Method." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/652.

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In this dissertation, we focus on solving Nonlinear Least Squares problems using a supervised approach. In particular, we developed a Supervised Descent Method (SDM), performed thorough theoretical analysis, and demonstrated its effectiveness on optimizing analytic functions, and four other real-world applications: Inverse Kinematics, Rigid Tracking, Face Alignment (frontal and multi-view), and 3D Object Pose Estimation. In Rigid Tracking, SDM was able to take advantage of more robust features, such as, HoG and SIFT. Those non-differentiable image features were out of consideration of previous
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5

Lösch, Felix. "Optimization of variability in software product lines a semi-automatic method for visualization, analysis, and restructuring of variability in software product lines." Berlin Logos-Verl, 2008. http://d-nb.info/992075904/04.

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6

Bai, Bing. "A Study of Adaptive Random Features Models in Machine Learning based on Metropolis Sampling." Thesis, KTH, Numerisk analys, NA, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293323.

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Artificial neural network (ANN) is a machine learning approach where parameters, i.e., frequency parameters and amplitude parameters, are learnt during the training process. Random features model is a special case of ANN that the structure of random features model is as same as ANN’s but the parameters’ learning processes are different. For random features model, the amplitude parameters are learnt during the training process but the frequency parameters are sampled from some distributions. If the frequency distribution of the random features model is well-chosen, both models can approximate d
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7

Sasse, Hugh Granville. "Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/4406.

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The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human pro
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8

YADAV, JYOTI. "A STUDY OF FEATURE OPTIMIZATION METHODS FOR LUNG CANCER DETECTION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19156.

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In this project, Lung cancer remains an extremely important disease in the world that causes deaths. Early Diagnosis can prevent large amounts of deaths. Classifiers play an important role in detecting lung cancer by means of a machine learning set of rules in addition to CAD-based image processing techniques. For the classifier’s accuracy, there is the need for a good feature collection of images. Features of an image can help to find all relevant information for identifying disease. Features are the important parameter for finding results. Mostly, features are extracted from featur
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9

Salehipour, Amir. "Combinatorial optimization methods for the (alpha,beta)-k Feature Set Problem." Thesis, 2019. http://hdl.handle.net/1959.13/1400399.

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Research Doctorate - Doctor of Philosophy (PhD)<br>This PhD research thesis proposes novel and efficient combinatorial optimization-based solution methods for the (alpha,beta)-k Feature Set Problem. The (alpha,beta)-k Feature Set Problem is a combinatorial optimization-based feature selection approach proposed in 2004, and has several applications in computational biology and Bioinformatics. The (alpha,beta)-k Feature Set Problem aims to select a minimum cost set of features such that similarities between entities of the same class and differences between entities of different classes are maxi
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10

Tayal, Aditya. "Effective and Efficient Optimization Methods for Kernel Based Classification Problems." Thesis, 2014. http://hdl.handle.net/10012/8334.

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Kernel methods are a popular choice in solving a number of problems in statistical machine learning. In this thesis, we propose new methods for two important kernel based classification problems: 1) learning from highly unbalanced large-scale datasets and 2) selecting a relevant subset of input features for a given kernel specification. The first problem is known as the rare class problem, which is characterized by a highly skewed or unbalanced class distribution. Unbalanced datasets can introduce significant bias in standard classification methods. In addition, due to the increase of data
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11

Rocha, de Paula Mateus. "Efficient methods of feature selection based on combinatorial optimization motivated by the analysis of large biological datasets." Thesis, 2013. http://hdl.handle.net/1959.13/938563.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Intuitively, the Feature Selection problem is to choose a subset of a given a set of features that best represents the whole in a particular aspect, preserving the original semantics of the variables on the given samples and classes. In practice, the objective of finding such a subset is often to reveal a particular characteristic present in the given samples. In 2004, a new feature selection approach was proposed. It was based on a combinatorial optimization problem called (α, β)-k-Feature Set Problem. The main advantage of using this approac
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12

Elmi, Carlo Alberto. "Design system integration for multi-objective optimization of aero engine combustors." Doctoral thesis, 2022. http://hdl.handle.net/2158/1276939.

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The transformation towards a climate-neutral civil aviation is providing significant business opportunities to the aero engine market players. To meet this target and keep competitiveness, however, groundbreaking solutions must be introduced at the product’s level in the shortest possible time. Industry lead-ers are increasingly embracing lean and digital approaches for this purpose, by applying these concepts at all company’s levels. Considerable room for im-provements can be identified in the development of complex components as, for instance, the combustor. Due to the complexity of phenomen
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13

"Structural optimization and engineering feature design with semi-Lagrangian level set method." 2013. http://library.cuhk.edu.hk/record=b5549808.

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基於計算機仿真的優化設計方法如今已成為產品設計的重要工具之一。其最主要特點包括縮短產品開發週期，降低物理實驗成本，保證產品質量以及利用科學方法推動設計創新等。與此同時，計算機輔助設計，仿真，優化的一體化策略也得到了學術界和工業界的廣泛關注。許多新的研究成果都致力於提高以往算法的效率和適用性。<br>基於水平集的形狀和拓撲優化算法是設計輕量化連續結構體的強有力的工具之一。相比於基於有限單元網格的材分佈算法，前者能夠更清楚地表a達所設計結構的幾何邊界和特徵。這個優勢使得該算法能更好的與計算機輔助幾何設計方法相結合，例如構造立體幾何法 (Constructive Solid Geometry)。另外，最新的研究表明，基於水平集的幾何表達方法能夠很好地與擴展有限元分析(Extended Finite ElementAnalysis) 相結合，實現高效的仿真優化計算。這種結合的主要特點包括統一的數據表達，高精度的結構分析和優化計算，以及優化過程中無需重新劃分有限單元網格等。<br>近年來，儘管水平集結構優化算法得到了廣泛的發展，許多基於該方法的應用也層出不窮，但仍有一些相對實際的問題亟待解決。例如，如何提高水平集優化效率，如何增強該方法的設計能力以及適用性等。本論文致力於研究上述問題并提出了一些實用的新方法。<br>首先，我們結合semi-Lagrangian 數值方法和最優化線搜索算法，提
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14

Yang, Yu Tai, and 楊御台. "A Hybrid Filter/Wrapper Method Using Simplified Swarm Optimization for Feature Selection in High-Dimensional Imbalanced Data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/21084379798551051101.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>104<br>In recent years, feature selection has become an important field in data mining and been wildly used in numerous regions. The purpose of feature selection is to search an optimal subset of features from existing data to maximize the accuracy. However, there are still few studies investigating the impact of data imbalance, the existence of underrepresented categories of data, on feature selection problem. Therefore, the aim of this study is to provide a feature selection method for increasing classifying high-dimensional imbalanced data accuracy. In this st
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15

Vazhbakht, Bahareh. "A finite element mesh optimization method incorporating geologic features for stress analysis of underground excavations." Thesis, 2011. http://spectrum.library.concordia.ca/35795/1/Vazhbakht_MASc_F2011.pdf.

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Application of numerical modeling in civil and mining engineering projects not only increases the effectiveness of analysis but also improves the results of the analysis. However, due to complexity of model generation and analysis, it still is a time consuming process. The finite element method requires a discretization, or a mesh, to solve the partial differential equations representing the problem. The finer and denser is the mesh, the more time and computer memory consuming is the analysis. Therefore, one of possible solutions is to simplify the analysis by reducing the mesh density while m
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