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 6 of 6 for “"Unsupervised feature learning"”.
-
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
… large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with GNNs. In the contrast learning step, all the samples in the 3D object dataset are cut into two parts …
-
Novel Architectures for Radar Sounder Signals Segmentation: From Convolutional Neural Networks to Quantum-Enhanced Networks
… are grounded in hybrid supervised deep learning architectures, unsupervised feature learning frameworks, and harnessing quantum machine learning frameworks to automatically segment geological units in the cryosphere subsurface. Firstly, we developed a supervised deep learning …
-
Language-independent methods for computer-assisted pronunciation training
… target L2 is also often needed to design a large feature set describing the deviation of nonnative speech from native speech. In contrast to machines, it is relatively easy for native listeners to detect pronunciation errors without being exposed to nonnative speech or trained with linguistic …
-
2022: A Computational Odyssey - Towards a Deeper Understanding of Clustering Streaming Human Activity Recognition Data
… modalities for deep analysis - pure supervised learning - are limited to the classes provided during training and are incapable of relaying any hidden knowledge residing outside of the trained labels. Unsupervised learning methods, such as clustering, are not only well equipped to handle …
-
Pushing the limits of traditional unsupervised learning
Unsupervised learning has important applications in extremely large data settings such as in medical, biological, social, and environmental data. Typically in these settings, copious amounts of data are collected, with the additional burden of high dimensionality and unavailability of class labels. …
-
Benchmarking authorship attribution techniques using over a thousand books by fifty Victorian era novelists
… the author of a given text and from the machine learning perspective, it can be seen as a classification problem. In the literature, there are a lot of classification methods for which feature extraction techniques are conducted. In this thesis, we explore information retrieval techniques such as …