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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 21–30 of 256 papers

TitleStatusHype
Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion AggregationCode0
Generalizable and Robust Spectral Method for Multi-view Representation LearningCode0
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning—0
Robust Variational Contrastive Learning for Partially View-unaligned ClusteringCode1
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
Dynamic Evidence Decoupling for Trusted Multi-view LearningCode0
Mammo-Clustering:A Weakly Supervised Multi-view Global-Local Context Clustering Network for Detection and Classification in Mammography—0
Steinmetz Neural Networks for Complex-Valued Data—0
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in EchocardiographyCode0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
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