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 9097 for “"Feature"”.
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Restricting Supervised Learning: Feature Selection and Feature Space Partition
… to solve either because of the redundant features or because of the structural complexity of the generative function. Redundant features increase the learning noise and therefore decrease the prediction performance. Additionally, a number of problems in various applications such as …
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Explicit Feature Relation and Implicit Feature Correlation Knowledge in Semantic Memory
… or theorybased knowledge in learning feature co-occurrences are attributable to incongruence between task type (speeded versus untimed) and knowledge type (statistically-based versus theory-based), though these knowledge types influence congruent tasks (McRae, de Sa & Seidenberg, 1997; …
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An examination of feature based modeling and systems utilizing feature techniques
Call number: LD2668 .R4 IE 1988 H36
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Feature selection from huge feature sets in the context of computer vision
Most learning systems use hand-picked sets of features as input data for their learning algorithms. This is particularly true of computer vision systems, where the number of features that can be computed over an image is, for practical purposes, limitless. Unfortunately, most of these features are …
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New optimization techniques for point feature and general curve feature based SLAM
This doctoral thesis deals with the feature based Simultaneous Localization and Mapping (SLAM) problem. SLAM as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the robot. In feature based …
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Rising: A Feature Film
… consists of a full-length screenplay for a feature film. A summary of the screenplay is as follows: Rising is the story of Ezra (16), an average boy from a middle class family, sent away to St Augustine’s Catholic boarding school for boys, due to his mother’s (Jeanette) promotion. Ezra soon …
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Sparse Kernel feature extraction
The presence of irrelevant features in training data is a significant obstacle for many machine learning tasks, since it can decrease accuracy, make it harder to understand the learned model and increase computational and memory requirements. One approach to this problem is to extract appropriate …
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Feature reinforcement learning agents
… actions. A recently proposed framework called Feature Markov Decision Process (PhiMDP) or Feature Reinforcement Learning (FRL) offers a sound and generic approach to addressing the GRL problem. The central idea in FRL is to find a good state representation function, which maps histories into …
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Feature reinforcement learning agents
… actions. A recently proposed framework called Feature Markov Decision Process (PhiMDP) or Feature Reinforcement Learning (FRL) offers a sound and generic approach to addressing the GRL problem. The central idea in FRL is to find a good state representation function, which maps histories into …
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Explanation-Based Feature Construction
… training data is limited. The crucial stage of feature construction, often done manually, plays a significant role in allowing such information to be incorporated into a learner. We propose algorithms for automated feature construction where available domain knowledge, even though imperfect and …
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Improving classification performance of microarray analysis by feature selection and feature extraction methods
In this study, we compared two feature extraction methods (PCA, PLS) and seven feature selection methods (mRMR and its variations, MaxRel, QPFS) on four different classifiers (SVM, RF, KNN, NN). We use ratio comparison validation for PCA method and 10-folds cross validation method for both the …
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Evolutionary feature creation for ensembles
Evolutionary feature creation for ensembles is about the generation of new attributes useful to build classifiers and ensembles of classifiers (EoC), based on evolutionary algorithms. The new attributes consist in transformations applied to the original raw features into a different space with the …
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Hybrid Methods for Feature Selection
<p>Feature selection is one of the important data preprocessing steps in data mining. The feature selection problem involves finding a feature subset such that a classification model built only with this subset would have better predictive accuracy than model built with a complete set of features. …
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Feature recognition in OCR text
This thesis investigates the recognition and extraction of special word sequences, representing concepts, from OCR text. Unlike general index terms, concepts can consist of one or more terms that combined, have higher retrieval value than the terms alone (i.e. acronyms, proper nouns, phrases). An …
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Facial feature representation and recognition
… two systems are developed to detect facial features, especially for eyes. The purpose is to develop a fast and reliable system to detect facial features automatically and correctly. By combining the proposed facial feature detection, the facial expression and neonatal pain recognition …
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Dee: A Feature Length Drama
<p>“DEE” is a feature length drama about a young addict who pursues a career in music in an attempt to keep her brother’s memory alive after his untimely death tears her family apart. The story explores how the characters deal with drug abuse, loss, broken families, toxic relationships, and …
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Grammatical feature data-oriented parsing
… thesis, we propose a backing-off to Grammatical Feature-DOP (GF-DOP). The GF-DOP model differs from Tree-DOP and LFG-DOP in that the trees are annotated with selected features extracted from the f-structure, rather than explicitly linked to corresponding f-structure units. In this way, we rnake …
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