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.
Results
Showing 1 to 19 of 19 for “"Feature reduction"”.
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Improving the Performance of a Hybrid Classification Method Using a Parallel Algorithm and a Novel Data Reduction Technique
… spectral class rejection) and a novel data reduction technique that can be used in conjuction with pIGSCR (parallel IGSCR). The parallel algorithm is motivated by a demonstrated need for more computing power driven by the increasing size of remote sensing datasets due to higher resolution …
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A Probabilistic Classification Algorithm With Soft Classification Output
… guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are …
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Hyperspectral Imagery for Precision Agriculture
… hyperspectral image processing is that a hybrid feature selection and feature extraction approach was proposed and implemented for image feature reduction. The general procedure was to select a subset of the original image bands and transform the image band subset to a new image space. The band …
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Classification of Carpiodes Using Fourier Descriptors: A Content Based Image Retrieval Approach
… based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known …
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A Machine Learning Classifiers Approach for Cardiovascular Disease Diagnosis
… platform were used. The data was cleaned, and 5 feature reduction techniques were investigated. Here, in addition a statistical unbiased ensemble feature reduction is proposed by imposing a unitary weight on all intersecting features. This Thesis study showed that by considering only 7 features, …
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Image segmentation and pattern classification using support vector machines
… challenging lower-level image processing tasks. Feature extraction, feature reduction, and classifier design based on selected features are the three essential issues for the pattern classification problem. In this dissertation, an automatic Seeded Region Growing (SRG) algorithm for color image …
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3D multiresolution statistical approaches for accelerated medical image and volume segmentation
… employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this …
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Supervised information retrieval for text and images
… engine can also be applied to text documents for feature reduction.
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Blind steganalysis using fractal features
… images with blind steganalysis using fractalfeatures has been proposed in this thesis. Two overarching methods were used to constructthe feature vector; first, using a variation of the Differential Box Counting algorithm forlacunarity estimation to extract the fractal features; and then, …
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Anomaly detection in hyperspectral signatures using automated derivative spectroscopy methods
… was carried out in four steps. They were: (1) feature extraction, (2) feature reduction through linear discriminant analysis, (3) performance characterization through receiver operating characteristic curves, and (4) signature classification using nearest mean and maximum likelihood …
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Development of environmentally benign microencapsulation with polymer microspheres and liposomes
… methods, these two methodologies feature reduction/prevention of using organic solvents, making them particularly attractive as green technology. For polymer microencapsulation, a novel in situ polymerization based process to encapsulate various types of fine particles, include …
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Dynamic analyses of malware
… ransomware accurately; creation of a logical feature reduction algorithm to minimise computational expense in machine learning; the first model in the literature which can differentiate between benign encryption (zipping) and malicious encryption. Lastly, the computational costs of 23 machine …
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Developing Computational Methods in Proximity Pharmacology for Enzyme Discovery, PROTAC Screening, and Conformational Space Exploration
… the exploration of complex landscapes; and (2) a feature reduction strategy using convolutional neural networks to reduce conformational space to a dimensionality that could be explored effectively. This thesis advances the field by identifying new opportunities in proximity pharmacology, …
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Design of a self-paced brain computer interface system using features extracted from three neurological phenomena
… of a 2-state SBCI system, 2) a two-stage feature reduction method for selecting wavelet coefficients extracted from movement-related potentials (MRP), 3) an SBCI system that classifies features extracted from three neurological phenomena: MRPs, changes in the power of the Mu and Beta …
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Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation
… how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5.
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Selected topics in statistical discriminant analysis.
… in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves the separation of the individual populations …
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A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain
… question is whether a subset of the input features (spectral bands) could be used without degrading classification accuracy. Our interest in feature extraction is twofold. First, we desire a significantly reduced set of features by which we can compress the signal. Second, we desire to …
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Coping With New Challengens for Density-Based Clustering
… sets contain a large number of measurements (or features) for a single data object. Usually, global feature reduction techniques cannot be applied to these data sets. Thus, the task of feature selection must be combined with and incooperated into the clustering process. In this thesis, we present …
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Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction
… <p>Protein secondary structures and other features of proteins are predicted efficiently, reliably, less expensively and more accurately. A novel method called Fast Learning Optimized PREDiction (FLOPRED) Methodology is proposed for predicting protein secondary structures and other …