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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 201–225 of 256 papers

TitleStatusHype
Anomaly detecting and ranking of the cloud computing platform by multi-view learning—0
Deep Multimodality Model for Multi-task Multi-view LearningCode0
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning—0
Learning Common Representation from RGB and Depth Images—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack DetectionCode0
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks—0
Robust Visual Tracking using Multi-Frame Multi-Feature Joint ModelingCode0
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning—0
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity TypingCode0
Improving Sentence Representations with Multi-view Frameworks—0
Canonical Correlation Analysis with Implicit Distributions—0
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization—0
ML-LocNet: Improving Object Localization with Multi-view Learning Network—0
Jointly Deep Multi-View Learning for Clustering Analysis—0
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering—0
Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank ModelsCode0
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views—0
Dynamic voting in multi-view learning for radiomics applications—0
Multi-view Sentence Representation Learning—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Dissimilarity-based representation for radiomics applications—0
Dual Memory Neural Computer for Asynchronous Two-view Sequential LearningCode0
Deep Multi-view Learning to Rank—0
A Survey on Multi-View Clustering—0
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