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 9 of 9 for “"correlated features"”.
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Probabilistic Correspondence Mapping for Audiovisual Speaker Modeling
… a new fusion scheme factorizes audio and visual features into correlated and uncorrelated ones. The correlated features are considered to be the correspondence between two modalities.
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… In the Bayesian Elastic Net, a small number of correlated features are identified for the response variable. In the sparse Factor Analysis for biomarker trajectories, the high dimensional gene expression data is reduced to a small number of latent factors, each with a prototypical dynamic …
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Explicit Feature Relation and Implicit Feature Correlation Knowledge in Semantic Memory
… theory statistics and shared variance between features were used to compare the relative influences of the two knowledge types in untimed relatedness ratings and speeded relatedness decisions for 65 feature pairs that span a range of correlational strength. Both knowledge types influenced both …
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A new feature engineering framework for financial cyber fraud detection using machine learning and deep learning
… framework that can produce the most effective features set for any ML and DL algorithms by taking both methods of feature engineering and features selection into a new framework. The framework consists of two main components: feature creation and feature selection. The purpose of feature …
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Semiparametric Approaches for Dimension Reduction Through Gradient Descent on Manifold
… Our methods show better performance for highly correlated features. We also develop ER-OPG and ER-MAVE to identify the basis of CS on a manifold. The entire conditional distribution of a response given predictors is estimated in a heterogeneous regression setting through composite expectile …
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Feature selection with a general hybrid algorithm
… problem involves discovering a subset of features, such that a classifier built only with this subset would have better predictive accuracy than a classifier built from the entire set of features. A large number of algorithms have already been proposed for the feature selection problem. …
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Investigating the Use of Artificial Intelligence in Chest Radiography: Efficacy, Bias, and Lessons from the COVID-19 Pandemic
… learning, where AI models rely on spuriously correlated features with the outcome rather than the underlying. Technical factors such as projection and positioning of the patient on chest radiography can introduce such biases. This thesis takes advantage of unprecedented data access and the …
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Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset
… summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic …
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Statistical Relational Learning for Proteomics: Function, Interactions and Evolution
… how to jointly improve the outputs of multiple correlated predictors of protein features by means of a very gen- eral probabilistic-logical consistency layer. The logical layer — based on grounding-specific Markov Logic networks [3] — enforces a set of weighted first-order rules encoding …