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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 226–250 of 256 papers

TitleStatusHype
Deep Multi-view Learning to Rank—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Partial Multi-View Learning—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
Deep Variational Canonical Correlation Analysis—0
Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations—0
Dissimilarity-based representation for radiomics applications—0
Diverse and Consistent Multi-view Networks for Semi-supervised Regression—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Dynamic voting in multi-view learning for radiomics applications—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
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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