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 106 for “"semi-supervised learning"”.
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Semi-supervised learning for natural language
Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. Unlabeled data has shown …
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Active and Semi-Supervised Learning for Speech Recognition
… to the combination of the rise in deep learning in speech recognition and an increase in computing power. The increase in computing power enabled the training of models on ever-expanding data sets, and deep learning allowed for the better exploitation of these large data sets. For …
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The information regularization framework for semi-supervised learning
… be missing the class label. While traditional supervised classifiers already have the ability to cope with some incomplete data, the new type of classifiers do not view unlabeled data as an anomaly, and can learn from data sets in which the large majority of training points are unlabeled. …
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Semi-Supervised Learning for Scalable and Robust Visual Search
… two classes of approaches: graph-based semi-supervised learning and hashing techniques. The graph-based approaches are used to improve accuracy, while hashing approaches are used to improve efficiency and cope with large-scale applications. A common theme shared between these two …
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Semi-supervised learning and relevance search on networked data
… two important and closely related problems, semi-supervised learning and relevance search, are studied on both homogeneous and heterogeneous networks. Different from many existing models, algorithms developed in this thesis are theoretically reasonable, widely applicable with minimum …
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Probabilistic models for multi-view semi-supervised learning and coding
… We investigate and develop novel multi view learning algorithms capable of learning from semi-supervised noisy sensor data, for automatically adapting to new users and working conditions, and for performing distributed feature selection on bandwidth limited sensor networks. We propose …
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Semi-supervised learning for acoustic and prosodic modeling in speech applications
… transcribed (labeled) data. We propose a unified semi-supervised learning framework for the problem of phone classification, phone recognition and prosody detection. The proposed approach will be particularly useful in the case where recognition performance is limited by the amount of transcribed …
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Minimal Labels, Maximum Gain. Image Classification with Graph-Based Semi-Supervised Learning
… last decade, the use and deployment of machine learning systems for computer vision has risen dramatically. To train a machine learning model it is often assumed that the practitioner has access to a large and representative labelled dataset from which they can optimise their model in a …
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COMPOSE: Compacted object sample extraction a framework for semi-supervised learning in nonstationary environments
… even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch.
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
Deep Learning, a growing sub-field of machine learning, has been applied with tremendous success in a variety of domains, opening opportunities for achieving human level performance in many applications. However, Deep Learning methods depend on large quantities of data with millions of annotated …
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Unraveling Complexity: Panoptic Segmentation in Cellular and Space Imagery
Advancements in machine learning, especially deep learning, have facilitated the creation of models capable of performing tasks previously thought impossible. This progress has opened new possibilities across diverse fields such as medical imaging and remote sensing. However, the performance of …
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Machine learning for network traffic classification under labeled data and training time constraints
This thesis investigates using machine learning (including deep learning) for network traffic classification when constrained by too little labeled data or insufficient time to train models from scratch. Network traffic classification is essential in network security, network management, and …
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Indoor place classification for intelligent mobile systems
… on commonly available sensors and machine learning based solutions which play a significant role in the research of place classification, solutions to train a machine to assign unknown instances with concepts understandable to human beings, like room, office and corridor, in both …
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Adversarial Learning based framework for Anomaly Detection in the context of Unmanned Aerial Systems
… definition of an anomaly is often ambiguous, unsupervised and semi-supervised deep learning (DL) algorithms that primarily use unlabeled datasets to model normal (regular) behaviors, are popularly studied in this context. The unmanned aerial system (UAS) can use contextual anomaly detection …
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Towards Uncovering the True Use of Unlabeled Data in Machine Learning
… data is a fundamental problem in machine learning. This dissertation provides contributions in different contexts, including semi-supervised learning, positive unlabeled learning and representation learning. In particular, we ask (i) whether is possible to learn a classifier in the context …
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Similarity modeling for machine learning
… is an important topic for both machine learning and computer vision. In this dissertation, we first propose a discriminative similarity learning method, then introduce two novel sparse similarity modeling methods for high dimensional data from the perspective of manifold learning and …
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Towards Object-based SLAM
… data are unavailable, it is important to enable semi-supervised learning to improve the robot’s performance with the unlabeled data collected by the robot itself. Second, after the objects are segmented, measurements for each object across different views have to be associated together for …
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Privileged Machine Learning for Prediction
Machine learning for prediction suffers from asymmetric distribution, such as posterior information, future information and hidden information. With some additional information only available in training, how to learn a machine learning model with them remains a key challenge. Despite recent …
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