Results 221 to 230 of about 5,726,851 (263)

AI-Driven Dental Procedure Coding: A Multi-Model Framework for CDT Extraction from Clinical Text. [PDF]

Dent J (Basel)
Annareddy P, Noori A, Kollipara D, Manda P.   +3 more
europepmc   +1 more source

From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial Intelligence in Dentistry-A Systematic Review. [PDF]

Healthcare (Basel)
Ardila CM, Vivares-Builes AM, Pineda-Vélez E.   +2 more
europepmc   +1 more source

Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review. [PDF]

Pharmaceuticals (Basel)
Reza MT, Kader MA, Inoubli W, Hossain MB, Islam MK.   +4 more
europepmc   +1 more source

Health Status Recognition of Yellow-Feathered Broilers in Floor-Rearing Environments via Whole-Body and Comb Feature Fusion. [PDF]

Animals (Basel)
Xue J, Hua T, Guan H, Bai H, Chang G, Li B.   +5 more
europepmc   +1 more source

Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance

ACM Transactions on Intelligent Systems and Technology, 2019
© 2019 Association for Computing Machinery. Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data.
Paul Kennedy, Ali Anaissi, Ali Braytee
exaly   +3 more sources

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Semi-supervised multi-label classification using incomplete label information

Neurocomputing, 2017
Abstract Classifying multi-label instances using incompletely labeled instances is one of the fundamental tasks in multi-label learning. Most existing methods regard this task as supervised weak-label learning problem and assume sufficient partially labeled instances are available.
Qiaoyu Tan, Guoxian Yu, Jun Wang
exaly   +2 more sources

Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Accepted by TPAMI.
Jie Wen, Min Zhang, Yong Xu
exaly   +5 more sources

Prompt-guided consistency learning for multi-label classification with incomplete labels

Neural Networks
Addressing insufficient supervision and improving model generalization are essential for multi-label classification with incomplete annotations, i.e., partial and single positive labels. Recent studies incorporate pseudo-labels to provide additional supervision and enhance model generalization. However, the noise in pseudo-labels generated by the model
Zhigang Zeng, Zhigang Zeng
exaly   +4 more sources

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels

Applied Intelligence, 2022
Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in ...
Zhi-Fen He, Chun-Hua Zhang, Bin Liu 0057, Bo Li 0023   +3 more
exaly   +4 more sources

Class label fusion guided correlation learning for incomplete multi-label classification

Information Fusion
Tingquan Deng, Changzhong Wang, Ming Yang   +2 more
exaly   +2 more sources