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 233 for “"Semi-supervised"”.
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Towards open world semi supervised detection
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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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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On Semi-supervised Estimation of Distributions
We study the problem of estimating the joint probability mass function (pmf) over two random variables. In particular, the estimation is based on the observation of 𝑚 samples containing both variables and 𝑛 samples missing one fixed variable. We adopt the minimax framework with [notation] loss …
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Semi supervised weighted maximum variance dimensionality reduction
… the 2P-WMV approach from our previous work to a semi-supervised version. The objective of this work is specially to show how two parameter version of Weighted Maximum Variance (2P-WMV) performs in Semi-Supervised environment in comparison to the supervised learning. By making use of both labeled …
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Reinforced co-learning for semi-supervised ranking
… applications, we are more often faced with the semi-supervised setting that only a partial set of data has labels. In this paper, we propose a co-learning strategy for the semi-supervised ranking problem. Our model has two modules: the classifier module and the reinforcement ranker module. Given …
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Semi-supervised detection of industrial fouling using ultrasound
… To this end, we extend existing literature on semi-supervised learning by presenting algorithms used to learn from a monotonic process, and model the high-dimensional signal data using a convolutional neural network that is highly robust to temporal variance. This thesis presents the machine …
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Projection methods for clustering and semi-supervised classification
… methods for the purposes of clustering and semi-supervised classification, with a primary focus on clustering. A number of contributions are presented which address this problem in a principled manner; using projection pursuit formulations to identify subspaces which contain useful …
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Active and Semi-Supervised Learning for Speech Recognition
… novel methods for active learning and for semi-supervised learning. For active learning, this thesis proposes a method based on a Bayesian framework termed NBest-BALD. NBest-BALD is based on Bayesian Active Learning by Disagreement (BALD). NBest-BALD selects utterances based on the mutual …
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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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Few-Shot Semi-Supervised Robust Text Classification with MAML
The need for few-shot semi-supervised text classification arises in a variety of applications, including, e.g., recommendation systems classifying textual content such as product descriptions or news articles based on limited amounts of user feedback. In such settings, existing supervised methods …
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Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations
Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and …
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A Semi-Supervised Information Extraction Framework for Large Redundant Corpora
… Extractions systems. The framework uses a semi-supervised approach to minimize human input. It also eliminates the need for external Named Entity Recognition systems by relying on freely available databases. The final result is a query-answering system which extracts information from large …
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Probabilistic models for multi-view semi-supervised learning and coding
… 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 probabilistic models built upon multi-view Gaussian …
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Graph-based approaches for semi-supervised and cross-domain sentiment analysis
… of labelled data necessary to carry out precise supervised sentiment classi cation. In response, research has moved towards developing semi-supervised and crossdomain techniques. Semi-supervised approaches still need some labelled data and their e ectiveness is largely determined by the amount of …
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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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Semi-Supervised Anomaly Detection and Heterogeneous Covariance Estimation for Gaussian Processes
… structure in the data. Finally, we introduce a semi-supervised method to incorporate expert input into a GP model. We are able to learn a probability surface defined over locations and responses based on sets of points labeled by an analyst as either anomalous or nominal. This allows us to …
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Semi-supervised and active training of conditional random fields for activity recognition
… time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In …
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