Divide-and-conquer ensemble self-training method based on probability difference
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-020-01971-7
Abstract: Self-training method can train an effective classifier by exploiting labeled instances and unlabeled instances. In the process of self-training method, the high confidence instances are usually selected iteratively and added to the training set for… read more here.
Keywords: training method; self training; probability difference;
Supervised training compared with no training or self-training in patients with subacromial pain syndrome: a systematic review and meta-analysis.
Sign Up to like & getrecommendations! Published in 2021 at "Archives of physical medicine and rehabilitation"
DOI: 10.1016/j.apmr.2021.03.027
Abstract: OBJECTIVE To study the effects of supervised training in adults with subacromial pain syndrome. DATA SOURCES EMBASE, MEDLINE, Cochrane Library, Cinahl, and Pedro were searched from inception to March 2020. STUDY SELECTION Independent reviewers selected… read more here.
Keywords: pain syndrome; training; self training; subacromial pain ... See more keywords
Self-training semi-supervised classification based on density peaks of data
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.05.072
Abstract: Abstract Having a multitude of unlabeled data and few labeled ones is a common problem in many practical applications. A successful methodology to tackle this problem is self-training semi-supervised classification. In this paper, we introduce… read more here.
Keywords: semi supervised; supervised classification; self training; training semi ... See more keywords
Self-training to improve UE function at the chronic stage post-stroke: a pilot randomized controlled trial
Sign Up to like & getrecommendations! Published in 2017 at "Disability and Rehabilitation"
DOI: 10.1080/09638288.2016.1239766
Abstract: Abstract Background and purpose: On-going practice and use of the weaker upper extremity (UE) are important for maintaining and improving function in individuals with chronic stroke. The effectiveness of two self-training programs for UE function… read more here.
Keywords: self; chronic stage; video games; self training ... See more keywords
Improving domain adaptation in de-identification of electronic health records through self-training
Sign Up to like & getrecommendations! Published in 2021 at "Journal of the American Medical Informatics Association : JAMIA"
DOI: 10.1093/jamia/ocab128
Abstract: OBJECTIVE De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where… read more here.
Keywords: domain adaptation; self training; identification; health ... See more keywords
An Effective Tumor Classification With Deep Forest and Self-Training
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3096241
Abstract: In recent years, tumor classification based on the gene expression omnibus has become a continuous attention field in the area of bioinformatics. Integration machine learning techniques are an efficient methods to solve these problems. Generally,… read more here.
Keywords: tumor classification; unlabelled samples; deep forest; self training ... See more keywords
Fast and Easy Sensor Adaptation With Self-Training
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3238875
Abstract: Object detectors based on deep neural networks have the disadvantage that new labels should be acquired whenever the complementary metal-oxide semiconductor (CMOS) image sensor (CIS) is changed. In this study, we propose a fast and… read more here.
Keywords: adaptation; self training; model; sensor adaptation ... See more keywords
Unsupervised Domain Adaptation for Remote Sensing Image Segmentation Based on Adversarial Learning and Self-Training
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2023.3278448
Abstract: There is a large amount of out-of-distribution (OOD) data in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from stable and reliable performance in real-world remote sensing applications.… read more here.
Keywords: domain; self training; image segmentation; remote sensing ... See more keywords
Self-Training Reinforced Adversarial Adaptation for Machine Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2022.3229344
Abstract: To alleviate the predicament of data annotating and the need for collecting data from identical distribution, unsupervised domain adaptation technologies have been widely deployed in the field of machine fault diagnosis. Nevertheless, most of them… read more here.
Keywords: fault diagnosis; adaptation; self training; adversarial adaptation ... See more keywords
A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2021.3055632
Abstract: In this article, we propose a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations (i.e., objects are labeled with points) to estimate both the center points and… read more here.
Keywords: point supervised; self training; training approach; point ... See more keywords
Self-Training for Class-Incremental Semantic Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3155746
Abstract: In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this… read more here.
Keywords: incremental semantic; self training; class incremental; semantic segmentation ... See more keywords