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 42 for “"subset selection"”.
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Subset Selection via Spectral Objectives
Selecting a small subset of items that captures key properties of a much larger data set is an important problem in a variety of areas, including machine learning, network design, and fair allocation of goods. We use a geometric model for this problem where the items (or their qualities) are …
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Applications of balance optimization subset selection
Balance Optimization Subset Selection (BOSS) is a framework designed to be used for causal inference on observational data. The theoretical foundation for the BOSS framework has been provided in the literature; this thesis aims to provide some examples of the practical value of BOSS by using it on …
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Subset Selection and Detection Problems in Opinion Dynamics Models
L'abstract è presente nell'allegato / the abstract is in the attachment
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New developments in causal inference using balance optimization subset selection
… As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation …
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Balance Optimization Subset Selection: a framework for causal inference with observational data
… dissertation describes the Balance Optimization Subset Selection (BOSS) framework to apply causal inference to observational data. BOSS is designed to identify the subset of observational data that is most appropriate for computing causal estimates. To do this, it compares the available treatment …
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Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency
… data sets with high dimensionality. Variable selection is an important procedure to extract useful knowledge from such complex data. While in many real-data applications, appropriate selection of variables should facilitate the model interpretation and computation efficiency. It is thus …
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Spatial Poisson regression : Bayesian approach correcting for measurement error with applications.
… questions of interest are answered using the subset selection procedure proposed by Bratcher and Bhalla (1974), used by Hamilton, Bratcher, and Stamey (2008) and Stamey, Bratcher, and Young (2007), to demonstrate the ease of use for combining the our model developed here and the subset …
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Regression under a modern optimization lens
… MIO. In Part I we propose a method to select a subset of variables to include in a linear regression model using continuous and integer optimization. Despite the natural formulation of subset selection as an optimization problem with an lo-norm constraint, current methods for subset selection do …
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Learning with generalized negative dependence : probabilistic models of diversity for machine learning
… experimental design and model compression: subset-selection problems that require carefully balancing the quality of each selected element with the diversity of the subset as a whole. Negative dependence, which models the behavior of "repelling" random variables, provides a rich mathematical …
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Effective Features and Machine Learning Methods for Document Classification
… which aims to capture low-dimensional feature subset that facilitates improved performance in text classification. The experimental results have demonstrated the advantages and usefulness of the proposed method for text classification in high-dimensional feature space in terms of the number of …
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Diversity-inducing probability measures for machine learning
Subset selection problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according …
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Semiparametric Bayesian Joint Model With Variable Selection
… we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against …
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OPTIMAL COMPUTING BUDGET ALLOCATION FOR SIMULATION BASED OPTIMIZATION AND COMPLEX DECISION MAKING
… It is not only an efficient ranking and selection procedure for simulation problems with finite candidate solutions but also an attractive concept of resource allocation under stochastic environment. In this thesis, the framework of optimal computing budget allocation is studied in detail …
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Essays on Semi-parametric Bayesian Econometric Methods
… analysis in Chapter 1. Chapter 3 focuses on the subset selection for the efficiencies of firms, which addresses the influence of heterogeneity in the distributions of efficiencies on subset selections by applying the semi-parametric Bayesian Random Effects Model introduced in Chapter 2.
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Scalable Inference Algorithms for Determinantal Point Processes
… (DPPs) are probability distributions on subsets of a collection of points that tend to generate diverse configurations of points. This feature makes them suitable as a probabilistic model of diversity. Recently this idea has been exploited extensively in subset selection problems, where …
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A regression-based approach for simulating feedfoward active noise control, with application to fluid-structure interaction problems
… is developed to detect and analyze collinearity. Subset selection, a numerical procedure for improving regressions, is shown to correspond to optimizing actuator locations for best control system performance. Exhaustive-search subset selection is used to optimize actuator locations for a sample …
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Integer optimization in data mining
… optimization algorithm, used to solve subset selection in regression and portfolio selection in asset allocation. We take advantage of the special structures of these problems by implementing a combination of implicit branch-and-bound, Lemke's pivoting method, variable deletion and …
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Representation learning for non-sequential data
… we formulate a new method for performing diverse subset selection using a neural set function approximation method. This method relies on the deep sets idea, which says that any set function s(X) has a universal approximator of the form f([sigma]x[xi]X [phi](x)). Second, we design a new …
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Ensemble-based Supervised Learning for Predicting Diabetes Onset
… and the accuracy of predictions through feature subset selection in order to predict diabetes onset. Data from a national health check programme (similar to NHS health check) was used. The aim is to predict diabetes onset better than other similar studies within the literature. For the …
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Sparse learning : statistical and optimization perspectives
… we explore an Lq-regularized version of the Best Subset selection procedure which mitigates the poor statistical performance of the best-subsets estimator in the low SNR regimes. The statistical and empirical properties of the estimator are explored, especially when compared to best-subsets …
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