Suchergebnisse - supervised clustering-labeling method

Unsupervised selective labeling for semi-supervised industrial defect detection von Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“… This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions …”
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Diagnosis-informed neuro-subtyping reveals subgroups of autism spectrum disorder with reliable and distinct functional connectivity profiles von Wang, Yaping, Chen, Zehua, Song, Peilun, Lam, Gary Yu-Hin, Kang, Xin, Wong, Patrick C.M., Geng, Xiujuan

“… We implemented neuro-subtyping of ASD using a semi-supervised clustering method, HeterogeneitY through DiscRiminative Analysis (HYDRA …”
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Self-supervised based clustering for retinal optical coherence tomography images von Luo, Yilong, Lin, Tian, Lin, Aidi, Mai, Xiaoting, Chen, Haoyu

“… ), diabetic macular edema (DME), drusen, and normal fundus. This study employed the Semantic Pseudo Labeling for Image Clustering (SPICE …”
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Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs von Zhong, Shan, Zeng, Jiahao, Yu, Yongxin, Lin, Bohong

“… This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC …”
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Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering von Almanza-Conejo, Oscar, Avina-Cervantes, Juan Gabriel, Garcia-Perez, Arturo, Ibarra-Manzano, Mario Alberto

“… ; some are used in treating psychological and physical disorders. This paper presents a method based on electroencephalogram signals analysis to classify multiple emotions recorded from subjects' gameplay seasons …”
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Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling von Ghoraani, Behnaz, Krishnan, Sridhar

“… , this article presents a novel direction to enhance the discriminatory power of pattern recognition methods …”
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A self-supervised deep learning framework for seismic facies segmentation von Li, Ming, Yan, Xue-song, Wu, Qing-hua

“… Traditional methods rely on costly manual labeling or simplistic clustering, while supervised deep learning struggles with limited labeled data …”
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Towards Cross-Environment Human Activity Recognition Based on Radar Without Source Data von Cao, Zhongping, Li, Zhenchang, Guo, Xuemei, Wang, Guoli

“… In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate …”
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Multi-Objective Progressive Clustering for Semi-Supervised Domain Adaptation in Speaker Verification von Li, Ze, Lin, Yuke, Jiang, Ning, Qin, Xiaoyi, Zhao, Guoqing, Wu, Haiying, Li, Ming

“… In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC …”
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INTERNATIONAL PATENT: TECH UNIVERSITY OF KOREA INDUSTRY ACADEMIC COOPERATION FOUNDATION, 한국공학대학교산학협력단 FILES APPLICATION FOR "SELF-SUPERVISED AND UNSUPERVISED LEARNING-BASED IMAGE CLUSTERING ELECTRONIC DEVICE FOR AUTOMATED LABELING AND NOISE SAMPLING OF PCB SURFACE DEFECT IMAGE AND OPERATION METHOD THEREOF"

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LogGzip: Towards log Parsing with lossless compression von Gao, Donghui, Liu, Changjian, Chen, Ningjiang, Hu, Xiaochun

“… Supervised learning parsers require labor-intensive manual data labeling. Clustering-based parsers, as an unsupervised method, minimize expert involvement and manual annotation …”
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Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment von Rangaprakash, D., Odemuyiwa, Toluwanimi, Narayana Dutt, D., Deshpande, Gopikrishna

“… ). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model …”
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Spatial-Spectral Bipartite Graph Clustering With Low-Frequency Tensor Regularization for Hyperspectral and LiDAR Data von Cao, Zhe, Lu, Yihang, Xin, Haonan, Yu, Chuanqiang, Wang, Rong, Nie, Feiping

“… Unlike supervised methods requiring costly expert annotations, unsupervised clustering eliminates labeling needs, thereby offering an efficient solution for complex scene analysis with reduced deployment costs …”
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Unsupervised Clustering in Football Analysis: A Color-Segmentation and Lighting Adaptation Approach von Pan, Weiwei, Zhou, Mian, Wang, Jifeng, Su, Jionglong, Stefanidis, Angelos

“… These methods reduce the effort needed for dataset labeling compared to supervised approaches …”
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Semi-supervised Emotional Music Generation Method Based on Improved Gaussian Mixture Variational Autoencoders von XU Bei, LIU Tong

“… emotions.Most existing emotional music generation technologies use the complete supervised methods based on emotion …”
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Discriminant non-stationary signal features' clustering using hard and fuzzy cluster labeling: Doc 394 von Ghoraani, Behnaz, Krishnan, Sridhar

“… , this article presents a novel direction to enhance the discriminatory power of pattern recognition methods …”
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Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber‐Reinforced Polymer Sandwich Composites Using Acoustic Emission von Gulsen, Abdulkadir, Kolukisa, Burak, Caliskan, Umut, Bakir‐Gungor, Burcu, Gungor, Vehbi Cagri

“… ‐supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes …”
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Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach von Manian, Vidya, Alfaro-Mejía, Estefanía, Tokars, Roger P.

“… A semi-supervised method for labeling and classification of hyperspectral images is presented …”
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SSLCNV: A Semi-supervised Learning Framework for Accurate Copy Number Variation Detection von Du, Ruchao, Dong, Jinxin, Jiang, Hua, Qi, Minyong, Zhang, Yuxi, Sun, Ranran, Xu, Mengke

“… To fully leverage the detection results of existing tools and improve the accuracy of CNV detection under complex sequencing conditions, a new method called SSLCNV …”
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The contrastive learning method in tongue image representation learning and its significance in intelligent diagnosis von Liu, Qi, Xu, Dan-dan, Yang, Peng, Liu, Quan-quan, Xia, Shuai-shuai, Zhang, Miao, Li, Ya-yun, Wang, Chun-bao, Peng, Qing-hua

“… in a self-supervised manner. Methods We applied clustering contrastive learning (CCL) to the representation learning of tongue images …”
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