Results 201 to 210 of about 273,362 (259)

The Clinical Significance of Key Pelvic Floor Ultrasound Indicators in POP Management: A Retrospective Study of Diagnostic Accuracy and Therapeutic Intervention. [PDF]

Int J Womens Health
Wang J, Zhang H, Zhu M, Liu T, Yan J, Shui C, Xu H, Ni R, Qin L, Ai Q, Wang B.   +10 more
europepmc   +1 more source

Comparative Efficacy of Canggui Tanxue Acupuncture Combined with Scapular Stabilization Training versus Scapular Stabilization Training Alone for Rotator Cuff Injury: A Retrospective Cohort Study. [PDF]

J Pain Res
Geng Y, Song M, Huang B, Lin R, Wu S, Lin A, Wang X.   +6 more
europepmc   +1 more source

Identification and Validation of Ferroptosis-Related Biomarkers and Therapeutic Targets in ARDS: A Bioinformatics and Experimental Study. [PDF]

J Inflamm Res
Dong B, Zhong B, Zuo J, Liao L, Zeng W, Xiong M, Wei Y, Zhang D, Fan X.   +8 more
europepmc   +1 more source

Multi-Omics Analysis Reveals m7G Methylation-Related Genes May Be Involved in TGF-β Signaling-Mediated Anti-PD-L1 Response in Bladder Cancer. [PDF]

Immunotargets Ther
Liang HQ, Li YJ, Yu JY, Mo QZ, Wei QJ, Liang YQ, Wei FY, Zhang XP, Zhao JW, He YX, Feng XD, Yin BT, Liang XW, Nong RJ, Wang YY, Qin M, Cheng JW.   +16 more
europepmc   +1 more source

Dynamic Changes in Implantable Collamer Lens Vault and Anterior Chamber Angle Under Varying Lighting Conditions. [PDF]

Clin Ophthalmol
Hirabayashi M, Cantu EC, Abramson AJ, Virdi G, Nasser T, Parkhurst G.   +5 more
europepmc   +1 more source

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Asymmetry label correlation for multi-label learning

Applied Intelligence, 2021
As an effective method for mining latent information between labels, label correlation is widely adopted by many scholars to model multi-label learning algorithms. Most existing multi-label algorithms usually ignore that the correlation between labels may be asymmetric while asymmetry correlation commonly exists in the real-world scenario.
Yusheng Cheng, Wang Yibin
exaly   +2 more sources

Label Distribution Learning by Exploiting Fuzzy Label Correlation

IEEE Transactions on Neural Networks and Learning Systems
Researchers have proposed to exploit label correlation to alleviate the exponential-size output space of label distribution learning (LDL). In particular, some have designed LDL methods to consider local label correlation. These methods roughly partition the training set into clusters and then exploit local label correlation on each one.
Xin Geng, Yuheng Jia, Jianhui Lv
exaly   +3 more sources

Multi-label feature selection based on correlation label enhancement

Information Sciences, 2023
Weiping Ding, Yaojin Lin, Lei Guo
exaly   +2 more sources

Multi-Label Learning by Exploiting Label Correlations Locally

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances.
Sheng-Jun Huang, Zhi-Hua Zhou
openaire   +1 more source