Results 221 to 230 of about 181,047 (259)

Correction to "Mitral regurgitation detection and central/eccentric classification using transformer-based deep learning in multi-view echocardiography". [PDF]

J Appl Clin Med Phys
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

A survey of multi-view machine learning

Neural Computing and Applications, 2013
Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine ...
Shiliang Sun, Sun Shiliang
exaly   +2 more sources

Comprehensive Multi-view Representation Learning

Information Fusion, 2023
Jihua Zhu, Zhiqiang Tian, Qinghai Zheng   +2 more
exaly   +2 more sources

Contrastive Multi-View Kernel Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel.
Jiyuan Liu 0003, Xinwang Liu 0002, Yuexiang Yang, Qing Liao 0001, Yuanqing Xia   +4 more
openaire   +2 more sources

Multi-view learning with Universum

Knowledge-Based Systems, 2014
The traditional Multi-view Learning (MVL) studies how to process patterns with multiple information sources. In practice, the MVL is proven to have a significant advantage over the Single-view Learning (SVL). But in most real-world cases, there are only single-source patterns to be dealt with and the existing MVL is unable to be directly applied.
Zhe Wang 0002, Yujin Zhu, Wenwen Liu, Zhihua Chen, Daqi Gao   +4 more
openaire   +1 more source

Multi-view representation learning for multi-view action recognition

Journal of Visual Communication and Image Representation, 2017
This approach directly exploits the relationships among different action categories from different views.We bridge the gap of the sparsity representation of different actions from the different views.This approach explores the task of cross-view recognition.
Tong Hao, Dan Wu, Qian Wang, Jinsheng Sun   +3 more
openaire   +1 more source

Multi-view feature engineering and learning

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
We frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. We show that, under very stringent conditions, these are related to “feature descriptors” commonly used in Computer Vision.
Jingming Dong, Nikolaos Karianakis, Damek Davis, Joshua Hernandez, Jonathan Balzer, Stefano Soatto   +5 more
openaire   +1 more source

Multi-view Proximity Learning for Clustering

2018
In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based ...
Kun-Yu Lin, Ling Huang 0002, Chang-Dong Wang 0001, Hong-Yang Chao   +3 more
openaire   +1 more source

Multi-view Transfer Learning with Adaboost

2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011
Transfer learning, serving as one of the most important research directions in machine learning, has been studied in various fields in recent years. In this paper, we integrate the theory of multi-view learning into transfer learning and propose a new algorithm named Multi-View Transfer Learning with Adaboost (MV-TL Adaboost).
Zhijie Xu, Shiliang Sun
openaire   +1 more source