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 41 for “"class labels"”.
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Semantic Mapping of Road Scenes
… eye view of the region with associated semantic labels for ten’s of kilometres of street level data. We generate the overhead semantic view from street level images. This is in contrast to existing approaches using satellite/overhead imagery for classification of urban region, allowing us to …
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Unsupervised Concept Drift Detection in Data Streams
… due to the high cost of collecting true class labels. Traditional detection methods usually need high computation and memory cost and is unable to distinguish between concept drift and novelty. To improve the drift detection efficiency, we propose four unsupervised concept drift detection …
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A Framework for Consistency Based Feature Selection
… two with the same feature values and the same class labels. This thesis introduces a new consistency-based algorithm, Automatic Hybrid Search (AHS) and reviews several existing feature selection algorithms (ES, PS and HS) which are based on the consistency rate. After that, we conclude this …
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External Support Vector Machine Clustering
… with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The …
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Visual Computing and AI Technologies to Analyse Movement Disorders Associated with Parkinson’s Disease for Diagnostic Purposes
… scores, providing motor symptom severity class labels for classification and correlation analysis. A real-time computer vision method used a custom-trained YOLO model to evaluate finger tapping videos, analysing computer features and their association with clinical ratings using Spearman …
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Protein Fold Recognition Using Adaboost Learning Strategy
… information. In this thesis, we present a novel classifier on protein fold recognition, using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen …
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Continual Learning for Deep Dense Prediction
… tasks are mainly studied in the context of image classification. In this work, we present a simple method to alleviate catastrophic forgetting for pixel-wise dense labeling problems. We build upon the regularization technique using knowledge distillation to minimize the discrepancy between the …
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Learning to visually predict terrain properties for planetary rovers
… sensor-based constraints are employed. A terrain classification method is proposed that exploits features from proprioceptive sensor data, and employs either a supervised support vector machine (SVM) or unsupervised k-means classifier to assign class labels to terrain patches that the rover has …
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Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)
… process. However, IDF is unaware to the training class labels and gives incorrect weight value to some features. Therefore, the proposed approach that is Modified Term Frequency – Inverse Document Frequency (MTF-IDF) algorithm give more focus on both sample and features to give correct weight …
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Natural Language Descriptions of Deep Visual Features
… categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons and behaviors in any network. Is a richer characterization of neuron-level computation possible? We introduce a procedure (called MILAN, for …
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Non-linear Multi Omics Data Integration Method Using Conditional Variational Autoencoders
… key advantage of this approach is that it allows class label information to be incorporated during the data integration process. To the best of our knowledge, CVAEs have not been applied in previous multi-omics research. Additionally, new methods for integrating more than two datasets using CVAEs …
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Exploratory Study of Fuzzy Clustering and Set-Distance Based Validation Indexes
… c-means clustering algorithm is tested on real classification and clus-tering datasets. Under classification datasets, Iris, Breast Cancer Wisconsin and Wine Recognitiondatasets are used. Water Treatment Plant and Libras Movement datasets are used as clusteringdatasets. In classification …
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classCleaner: A Quantitative Method for Validating Peptide Identification in LC-MS/MS Workflows
… primary interest. In this dissertation I present classCleaner, a novel algorithm designed to identify misidentified peptides from each protein using the available quantitative data. The algorithm is based on the idea that distances between peptides belonging to the same protein are stochastically …
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Grounding robot motion in natural language and visual perception
… in which objects are differentiated by abstract class labels.</p> <p>Finally, I present work that unifies the previous two approaches. This method detects, localizes, and labels objects, as the previous method does. However, this new method integrates natural-language descriptions to learn actual …
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Robust Domain Adaptation Using Active Learning
… on the training dataset fails to produce good classification accuracy on the test dataset. One way to mitigate this problem is to use domain adaptation techniques; these techniques build a new model on the unlabeled test dataset (target dataset) by transferring information from a related but …
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Improving Computation for Hierarchical Bayesian Spatial Gaussian Mixture Models with Application to the Analysis of THz image of Breast Tumor
… priors to account for the autocorrelation in the classifications. However, the inclusion of spatially correlated Gaussian processes results in a computational burden which is resolved by applying a P\`{o}lya-gamma data augmentation scheme that results in improved fit of the GMM in spatially …
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