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 7 of 7 for “"multi-view learning"”.
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Probabilistic models for multi-view semi-supervised learning and coding
… investigates the problem of classification from multiple noisy sensors or modalities. Examples include speech and gesture interfaces and multi-camera distributed sensor networks. Reliable recognition in such settings hinges upon the ability to learn accurate classification models in the face of …
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New Methodology for Measuring Semantic Functional Similarity Based on Bidirectional Integration
… scoring matrices derived from principles of multi-view learning in machine learning algorithm and five different databases including Gene Ontology, UniProt, SCOP, CATH, and KUPS. The proposed method also shows how diverse databases and principles in machine learning theory can be integrated …
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Statistical Surrogate Models for Robust Design Optimisation in Reduced Dimension
… of complex engineering products is crucial for multi-query problems such as design optimisation. However, the design of these products depends on complex and often deterministic computational models that may be expensive-to-evaluate. Consequently, it is expedient to consider these models as …
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Heterogeneous machine learning: characterization, generation and comprehension
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms
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Learning Based Objective QoE Models Across Interactive and Immersive Media
Delivering high-quality multimedia experiences at scale requires objective Quality of Experience (QoE) models that reliably approximate human perception across heterogeneous contents, devices, and network conditions. Subjective user studies remain the reference standard for assessing perceived …
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Unsupervised feature analysis for high dimensional big data
… is not available, traditional supervised learning can not be directly applied so we need to study unsupervised methods which could work well even without supervision. Feature analysis has been proven effective and important for many applications. Feature analysis is a broad research field, …
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Advances in nonnegative matrix factorization with application on data clustering.
… important direction in many fields, e.g., machine learning, data mining and computer vision. It aims to divide data into groups (clusters) for the purposes of summarization or improved understanding. With the rapid development of new technology, high-dimensional data become very common in many real …