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MULTI-VIEW LEARNING

Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.

Source: Dissimilarity-based representation for radiomics applications

Papers

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Showing 201–210 of 256 papers

TitleStatusHype
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss—0
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions—0
Multi-view Regularized Gaussian Processes—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
Multi-View representation learning in Multi-Task Scene—0
Multi-view Sentence Representation Learning—0
Multi-view Subspace Adaptive Learning via Autoencoder and Attention—0
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment—0
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation—0
Neural News Recommendation with Heterogeneous User Behavior—0
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