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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 176–200 of 256 papers

TitleStatusHype
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning—0
MultiEarth 2023 Deforestation Challenge -- Team FOREVER—0
Multimodal and Multi-view Models for Emotion Recognition—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multi-view Audio and Music Classification—0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Multi-View Class Incremental Learning—0
Multi-view Data Classification with a Label-driven Auto-weighted Strategy—0
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views—0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency—0
Multi-view Instance Attention Fusion Network for classification—0
Multi-View Intact Space Learning—0
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis—0
Multi-view learning for automatic classification of multi-wavelength auroral images—0
Multi-view learning for multivariate performance measures optimization—0
Multi-View Learning for Vision-and-Language Navigation—0
Multi-View Learning for Web Spam Detection—0
Multi-View Learning of Word Embeddings via CCA—0
Multi-view learning with privileged weighted twin support vector machine—0
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation—0
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds—0
Multi-view metric learning for multi-instance image classification—0
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
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