Semi-supervised learning with connectivity-driven convolutional neural networks-Reference-Cited by-同舟云学术

Semi-supervised learning with connectivity-driven convolutional neural networks

Published:2019-12 Issue: Volume:128 Page:16-22
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Amorim Willian Paraguassu,Rosa Gustavo Henrique,Thomazella Rogério^ORCID,Castanho José Eduardo Cogo,Dotto Fábio Romano Lofrano,Júnior Oswaldo Pons Rodrigues,Marana Aparecido Nilceu,Papa João Paulo^ORCID

Funder

CNPq

FAPESP

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. Semi-supervised pattern classification using optimum-path forest;Amorim,2014

2. Multi-label semi-supervised classification through optimum-path forest;Amorim;Inf. Sci.,2018

3. Improving semi-supervised learning through optimum connectivity;Amorim;Pattern Recognit.,2016

4. Semi-supervised clustering by seeding;Basu,2002

5. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples;Belkin;J. Mach. Learn. Res.,2006

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Human-in-the-loop: Using classifier decision boundary maps to improve pseudo labels;Computers & Graphics;2024-11

2. ABAE: Auxiliary Balanced AutoEncoder for class-imbalanced semi-supervised learning;Pattern Recognition Letters;2024-06

3. Linking Data Separation, Visual Separation, Classifier Performance Using Multidimensional Projections;Communications in Computer and Information Science;2024

4. Measuring the quality of projections of high-dimensional labeled data;Computers & Graphics;2023-11

5. Deep feature annotation by iterative meta-pseudo-labeling on 2D projections;Pattern Recognition;2023-09