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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 241–250 of 256 papers

TitleStatusHype
Factorized Latent Spaces with Structured Sparsity—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
Fine-Tuning Language Models with Reward Learning on Policy—0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Group-sparse Embeddings in Collective Matrix Factorization—0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
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