云南高校图书馆联盟文献共享服务平台- CO-TRAINING

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EACNet:Ensemble adversarial co-training neural network for handling missing modalities in MRI images for brain tumor segmentation: 《Journal of Measurement Science and Instrumentation》2025年第1期11-25,共15页RAMADHAN Amran Juma CHEN Jing PENG Junlan; supported by Gansu Natural Science Foundation Programme(No.24JRRA231);National Natural Science Foundation of China(No.62061023);Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。; Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a co...; 关键词：deep learning magnetic resonance imaging(MRI) medical image analysis semantic segmentation segmentation accuracy image synthesis

Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance: 《npj Computational Materials》2024年第1期1103-1115,共13页Wei Xu Rui Wang Chunhai Hu Guilin Wen Junqi Cui Longjiang Zheng Zhen Sun Yungang Zhang Zhiguo Zhang; supported by the National Natural Science Foundation of China(NSFC Nos.12204401,12332002,and 62175208).; Near-infrared(NIR)phosphors based on Cr3+doped garnets present great potential in the next generation of NIR light sources.Nevertheless,the huge searching space for the garnet composition makes the rapid discovery of ...; 关键词：enable PERFORMANCE establishing

Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array: 《Journal of Ocean University of China》2023年第5期1201-1215,共15页ZHOU Xingyue YANG Kunde YAN Yonghong LI Zipeng DUAN Shunli; the National Natural Science Foundation of China(No.6210011631);in part by the China Postdoctoral Science Foundation(No.2021M692628)。; The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driv...; 关键词：underwater acoustic target recognition marine acoustic signal processing sound field feature extraction sparse adversarial network

Minimax entropy-based co-training for fault diagnosis of blast furnace被引量：1: 《Chinese Journal of Chemical Engineering》2023年第7期231-239,共9页Dali Gao Chunjie Yang Bo Yang Yu Chen Ruilong Deng; supported in part by the National Natural Science Foundation of China(61933015);in part by the Central University Basic Research Fund of China under Grant K20200002(for NGICS Platform,Zhejiang University)。; Due to the problems of few fault samples and large data fluctuations in the blast furnace(BF)ironmaking process,some transfer learning-based fault diagnosis methods are proposed.The vast majority of such methods perfo...; 关键词：CO-TRAINING Fault diagnosis Blast furnace Minimax entropy Transfer learning

CNN-Based Broad Learning for Cross-Domain Emotion Classification被引量：2: 《Tsinghua Science and Technology》2023年第2期360-369,共10页Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou; This work was partially supported by the National Natural Science Foundation of China(No.61876205);the Natural Science Foundation of Guangdong(No.2021A1515012652);the Science and Technology Program of Guangzhou(No.2019050001).; Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised manner.Due to the domain discrepancy,...; 关键词：cross-domain emotion classification CNN broad learning CLASSIFIER CO-TRAINING

A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets被引量：1: 《Energy and AI》2021年第2期172-180,共9页Yunmin Liang Zhichun Liu Wei Liu; The research was financially supported by the National Natural Sci-ence Foundation of China(Nos.51776079 and 51736004).; Predicting the thermal conductivity of polymeric composites filled with BN sheets is helpful for fabricating ther-mal management material.In this study,a co-training style semi-supervised artificial neural network mod...; 关键词：Polymeric composites BN sheets Semi-supervised regression Thermal conductivity Artificial neural network

基于多视图的文本聚类改进方法被引量：3: 《浙江工业大学学报》2021年第1期1-8,共8页王卫红李樊金凌剑; 浙江省自然科学基金资助项目(LZ14F020001)。; 近年来,随着自然语言处理技术的发展,聚类技术在文本处理领域中的作用愈发凸显。目前,国内多视图文本聚类的相关研究进展仍处于起步阶段,通常运用的聚类方法是基于文本的单一领域来展现特定方面的聚类情况,但越来越多的文本聚类研究从...; 关键词：文本聚类 LDA TF-WIDF CO-TRAINING 谱聚类

RFID indoor positioning based on semi-supervised actor-critic co-training: 《The Journal of China Universities of Posts and Telecommunications》2020年第5期69-81,共13页Li Li Zheng Jiali Quan Yixuan Lin Zihan Li Yingchao Huang Tianxing; the National Natural Science Foundation of China(61761004);the Natural Science Foundation of Guangxi Province,China(2019GXNSFAA245045)。; For large-scale radio frequency identification(RFID) indoor positioning system, the positioning scale is relatively large, with less labeled data and more unlabeled data, and it is easily affected by multipath and whi...; 关键词：RFID RSSI semi-supervised actor-critic Kronecker-Factored CO-TRAINING

基于Co-training协同训练的在线虚假评论识别研究被引量：8: 《系统工程理论与实践》2020年第10期2669-2683,共15页张文王强步超骐李健张思光; 国家自然科学基金(71932002,71601023,61432001);西安市科技计划创新基金(文理专项,2016CXWL21)。; 本文基于协同训练模型(co-training)提出了一种新的在线虚假评论识别方法CoDeRI以解决虚假评论识别中模型训练数据不足的问题.对同一评论信息,本文通过构建两个特征视图相互学习以识别虚假评论信息:视图一的特征来自于评论文本的词项(Te...; 关键词：协同训练概率上下文无关文法虚假评论朴素贝叶斯 CoDeRI

基于协同训练的意图分类优化方法被引量：4: 《现代情报》2019年第5期57-63,73,共8页邱云飞刘聪; [目的/意义]针对单纯使用统计自然语言处理技术对社交网络上产生的短文本数据进行意向分类时存在的特征稀疏、语义模糊和标记数据不足等问题,提出了一种融合心理语言学信息的Co-training意图分类方法。[方法/过程]首先,为丰富语义信息,...; 关键词：社交网络意图分类心理语言学协同训练(Co-training)

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