Výsledky vyhľadávania - Stacked semi-supervised autoencoder

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

“…In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network…”
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A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification Autor Aouedi, Ons, Piamrat, Kandaraj, Bagadthey, Dhruvjyoti

“… 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

“…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

“… 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

“…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

“…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

“…, 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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Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection Autor Jagat, Rikhi Ram, Sisodia, Dilip Singh, Singh, Pradeep

“… 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

“… 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

“… 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

“…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

“…•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

“… 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

“…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

“… 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

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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

“…•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

“…In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals…”
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US Patent Issued to Koninklijke Philips on Jan. 3 for "Semi-supervised classification with stacked autoencoder" (Massachusetts, New York, New Hampshire Inventors)

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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

“…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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