Výsledky vyhledávání - Stacked semi-supervised autoencoder

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    Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery Autor Protopapadakis, Eftychios, Doulamis, Anastasios, Doulamis, Nikolaos, Maltezos, Evangelos

    ISSN: 2072-4292, 2072-4292
    Vydáno: MDPI AG 01.02.2021
    Vydáno v Remote sensing (Basel, Switzerland) (01.02.2021)
    “…In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network…”
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    Journal Article
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    A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification Autor Aouedi, Ons, Piamrat, Kandaraj, Bagadthey, Dhruvjyoti

    ISSN: 2643-3303
    Vydáno: IEEE 13.10.2020
    “… To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data…”
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    Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace Autor Zhou, Ping, Zhao, Peng, Ou, Zihui, Chai, Tianyou

    ISSN: 0941-0643, 1433-3058
    Vydáno: London Springer London 01.06.2025
    Vydáno v Neural computing & applications (01.06.2025)
    “…This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs…”
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    A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks Autor Lai, Jie, Wang, Xiaodan, Xiang, Qian, Quan, Wen, Song, Yafei

    ISSN: 1099-4300, 1099-4300
    Vydáno: Basel MDPI AG 30.08.2023
    Vydáno v Entropy (Basel, Switzerland) (30.08.2023)
    “… However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems…”
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    Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection Autor Yuan, Chengsheng, Chen, Xianyi, Yu, Peipeng, Meng, Ruohan, Cheng, Weijin, Wu, Q. M. Jonathan, Sun, Xingming

    ISSN: 1861-8200, 1861-8219
    Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020
    Vydáno v Journal of real-time image processing (01.02.2020)
    “…The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are…”
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    Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals Autor Gogna, Anupriya, Majumdar, Angshul, Ward, Rabab

    ISSN: 0018-9294, 1558-2531
    Vydáno: United States IEEE 01.09.2017
    “…Objective: An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed…”
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    Novel semi-supervised sparse stacked autoencoder integrated with local linear embedding for industrial soft sensing Autor He, Yan-Lin, Jiang, Yu, Gao, Hui-Hui, Xu, Yuan, Zhu, Qun-Xiong

    ISSN: 0019-0578, 1879-2022, 1879-2022
    Vydáno: United States Elsevier Ltd 04.06.2025
    Vydáno v ISA transactions (04.06.2025)
    “…, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE…”
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    Journal Article
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    Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection Autor Jagat, Rikhi Ram, Sisodia, Dilip Singh, Singh, Pradeep

    ISSN: 0941-0643, 1433-3058
    Vydáno: London Springer London 01.08.2023
    Vydáno v Neural computing & applications (01.08.2023)
    “… To address the aforementioned issues, we reformulated web robot detection as a semi-supervised…”
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    Representation learning via a semi-supervised stacked distance autoencoder for image classification Autor Hou, Liang, Luo, Xiao-yi, Wang, Zi-yang, Liang, Jun

    ISSN: 2095-9184, 2095-9230
    Vydáno: Hangzhou Zhejiang University Press 01.07.2020
    “… The model is called a semi-supervised distance autoencoder. Each layer is first pre-trained in an unsupervised manner…”
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    Ground Target Recognition Using Carrier-Free UWB Radar Sensor With a Semi-Supervised Stacked Convolutional Denoising Autoencoder Autor Zhu, Yuying, Zhang, Shuning, Li, Xiaoxiong, Zhao, Huichang, Zhu, Lingzhi, Chen, Si

    ISSN: 1530-437X, 1558-1748
    Vydáno: New York IEEE 15.09.2021
    Vydáno v IEEE sensors journal (15.09.2021)
    “… Feature extraction is fundamental and crucial for target recognition. In this paper a deep network named semi-supervised stacked convolutional denoising autoencoder (SCDAE…”
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    Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder Autor Aouedi, Ons, Piamrat, Kandaraj, Bagadthey, Dhruvjyoti

    ISSN: 1389-1286, 1872-7069
    Vydáno: Amsterdam Elsevier B.V 22.04.2022
    “…Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era…”
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    A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes Autor Yuan, Xiaofeng, Ou, Chen, Wang, Yalin, Yang, Chunhua, Gui, Weihua

    ISSN: 0009-2509, 1873-4405
    Vydáno: Elsevier Ltd 18.05.2020
    Vydáno v Chemical engineering science (18.05.2020)
    “…•A semi-supervised autoencoder (SS-AE) is first developed as the basic network to extract quality-related features…”
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    Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals Autor Luo, Junhai, Tian, Yuxin, Yu, Hang, Chen, Yu, Wu, Man

    ISSN: 1099-4300, 1099-4300
    Vydáno: Switzerland MDPI AG 20.04.2022
    Vydáno v Entropy (Basel, Switzerland) (20.04.2022)
    “… To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA…”
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    Stacked Dual-Guided Autoencoder: A Scalable Deep Latent Variable Model for Semi-Supervised Industrial Soft Sensing Autor Yang, Zeyu, Hu, Tingting, Yao, Le, Ye, Lingjian, Qiu, Yi, Du, Shuxin

    ISSN: 0018-9456, 1557-9662
    Vydáno: New York IEEE 2024
    “…Stacked autoencoders (SAEs) have been widely used in soft sensing of industrial process data…”
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    Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder Autor Chen, Junjian, Yu, Zhuliang, Gu, Zhenghui

    ISSN: 1742-6588, 1742-6596
    Vydáno: Bristol IOP Publishing 01.09.2020
    Vydáno v Journal of physics. Conference series (01.09.2020)
    “… To address this problem, we propose a semi-supervised deep learning method based on the stacked variational autoencoder (SVAE…”
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    Semi-Supervised Classification With Stacked Autoencoder

    Vydáno: Washington, D.C Targeted News Service 03.01.2023
    Vydáno v Targeted News Service (03.01.2023)
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    Newsletter
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    Rock mass type prediction for tunnel boring machine using a novel semi-supervised method Autor Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Xiao, Dengyu, Sun, Hao, Liu, Chengliang

    ISSN: 0263-2241, 1873-412X
    Vydáno: London Elsevier Ltd 01.07.2021
    “…•A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face…”
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    Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals Autor Gogna, Anupriya, Majumdar, Angshul, Ward, Rabab

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 11.12.2019
    Vydáno v arXiv.org (11.12.2019)
    “…In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals…”
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    Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using Stacked Sparse Autoencoder Autor Jabason, Emimal, Ahmad, M. Omair, Swamy, M. N. S

    Vydáno: IEEE 01.10.2018
    “…In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been…”
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