Bibliografías temáticas / FEATURE OPTIMIZATION METHODS

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Literatura académica sobre el tema "FEATURE OPTIMIZATION METHODS"

Autor: Grafiati
Publicado: 11 de septiembre de 2023

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Tesis sobre el tema "FEATURE OPTIMIZATION METHODS"

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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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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