Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"regularization method"”.
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The gluon self-energy in cavity quantum chromodynamics
… is closely related to free space dimensional regularization. In this cavity regularization method, the energy shift is expressed as the integral of a divergent spectral function, from which the divergence may be extracted by analogy to the free space expression. It is shown for the case of the …
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Fine-grained artworks classification
… and year classification. We also propose a regularization method that penalizes correlations of convolutional feature maps. With the decorrelation regularization, we further improve the classification accuracy of the proposed architecture.
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INVERSE METHOD FOR DETERMINING TEMPERATURE DISTRIBUTION IN MACHINING
… in the estimated boundary temperatures. The regularization method is used to make the present steady-state two-dimensional inverse heat conduction problem well-posed. The regularization method modifies the least squares approach by adding the regularization terms which are controlled by …
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Calibration of Option Pricing in Reproducing Kernel Hilbert Space
… problem is shown to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility function which allows us to embrace both the financial and time factors of …
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REGULARIZATION ON MACHINE LEARNING
… occurs. To achieve better generalization, many regularization methods were proposed to reduce overfitting. In this thesis, we propose a simple-yet-effective regularization method called Drop-Activation. At the training phase, we drop nonlinear activation functions randomly and set them to be …
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The information regularization framework for semi-supervised learning
… This thesis is a study of the information regularization method for semi-supervised classification, a unified framework that encompasses many of the common approaches to semi-supervised learning, including parametric models of incomplete data, harmonic graph regularization, redundancy of …
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New Developments on Quantitative Imaging Using Ultrasonic Waves
… and backscatter coefficient analysis. First, a regularization method was developed to improve on the computational stability of acoustic tomography. The use of multiple frequency information to extend the region of convergence of acoustic tomography was validated experimentally. Second, the …
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Modeling and inversion of self-potential data
… The central component of this work describes a methodology for inverting self-potential data to recover the three-dimensional distribution of causative sources in the earth. This approach is general in that it is not specific to a particular forcing mechanism, and is therefore applicable to a …
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Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
… this study introduces an absolute correlation regularization method, which is evaluated with the synthetic and the real-world data. The results demonstrate the prevalence of prediction disparity in travel behavior modeling, which can exacerbate social inequity if the prediction results without …
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Progettazione di un device di imaging tomografico, basato su principio impedenziometrico, per la valutazione di cicli cardio-respiratori in atleti
… is an under-determined problem requiring a regularization method. The objective of this thesis was to develop a device for EIT imaging, which could be convenient, portable, inexpensive, noninvasive and easily programmable. A simple EIT system and its hardware parts has been developed. A belt …
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Automatic model construction with Gaussian processes
This thesis develops a method for automatically constructing, visualizing and describing a large class of models, useful for forecasting and finding structure in domains such as time series, geological formations, and physical dynamics. These models, based on Gaussian processes, can capture many …
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Advanced Techniques For Prediction of Forest Above Ground Biomass Using Satellite Remote Sensing Data
… A systematic framework involving an adaptive regularization method was implemented to observe and quantify the response linked to various characteristics of the SMS data. The second contribution presents a dynamic generative neural network architecture for modelling AGB using multi-sensor …
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Probing localized states distributions in semiconductors by Laplace transform transient photocurrent spectroscopy
… of the existing mathematically approximate methods (Naito H. <i>et al</i>, 1996; Nagase T. <i>et al</i>, 1999; Ogawa N. <i>et al</i>, 2000) based on the <i>Laplace </i>transformation for solving the MT system of equations is given. One of the main objectives of this work was to develop an …
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Network Analysis of the Financial Sector: A Comprehensive Perspective with Adaptive Joint LASSO Method
… event analysis framework? My thesis provides methodological and conceptual contributions to financial network analysis. The contribution of my thesis to the existing literature is threefold: 1. I propose a new regularization method, the adaptive joint least absolute shrinkage and selection …
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Functional regression models in the frame work of reproducing kernel Hilbert space
… selection and estimation technique. A novel regularization method called the Grouped Smoothly Clipped Absolute Deviation (GSCAD) is proposed. The initial problem can be transferred into a dictionary learning problem, where the GSCAD can be directly applied to simultaneously learn a sparse …
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New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles
… DWT vectors is estimated using a matrix-regularization method; then the DWT vectors are aggregated (batched) so that the nonoverlapping batch means of the reduced-dimension DWT vectors have manageable covariances. To monitor shifts in the mean profile during Phase II operation, WDFTC …
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Development and implementation of efficient noise suppression methods for emission computed tomography
… in photon detection. Iterative reconstruction methods rely on regularization terms to suppress image noise and render radiotracer distribution with good image quality. The choice of regularization method substantially affects the appearances of reconstructed images, and is thus a critical …
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Choice Modeling and Assortment Optimization on the Transformer Model
… on the training data, we use dropout as the regularization method during training. We compare our model to both traditional choice models (the multinomial logit model and its synergistic variant that considers cross-product interaction) and machine learning-based choice models (decision …
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Interpreting Deep Neural Networks and Beyond: Visualization, Learning Dynamics, and Disentanglement
… a better understanding of current deep learning methods will inspire the development of more principled computational approaches with better robustness and interpretability. In this thesis, we focus on interpreting and improving deep neural networks from the following three perspectives: …
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Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference
… demanding. This thesis focuses on Bayesian methods for inverse problems governed by partial differential equations and for simulation-based (likelihood-free) inference: in both settings, the high dimensionality of model parameters and/or data can render naïve posterior exploration …
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