A Multi-Rate Probabilistic Slow Feature Regression Model for Dynamic Feature Learning and Industrial Soft Sensor Development by Miao Zhang, Zhiwei Wen, Le Zhou

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ES²FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images by Bing Liu, Kuiliang Gao, Anzhu Yu, Lei Ding, Chunping Qiu, Jia Li

“…Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>FL) method is proposed in this paper. …”
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Semi-Supervised Domain Adaptation Using Explicit Class-Wise Matching for Domain-Invariant and Class-Discriminative Feature Learning by Ba Hung Ngo, Jae Hyeon Park, So Jeong Park, Sung In Cho

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A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning by Gang Wang, Jinyue Hu, Yu Zhang, Zhaolin Xiao, Mengxing Huang, Zhanping He, Jing Chen, Zhiming Bai

“…This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. …”
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Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs by Ping Xuan, Jinshan Xiu, Hui Cui, Xiaowen Zhang, Toshiya Nakaguchi, Tiangang Zhang

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PM-Net: A Multi-Level Keypoints Detector and Patch Feature Learning Network for Optical and SAR Image Matching by Ziqian Li, Zhitao Fu, Han Nie, Sijing Chen

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Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning by Yaping Hao, Qiang Gao

“…In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. …”
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Dempster–Shafer evidence theory‐based multi‐feature learning and fusion method for non‐rigid 3D model retrieval by Hui Zeng, Ran Zhang, Xiuqing Wang, Dongmei Fu, Qingting Wei

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A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring by Jiayu Ou, Hongkun Li, Gangjin Huang, Qiang Zhou

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Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach by Ryoichi Takashima, Yuya Sawa, Ryo Aihara, Tetsuya Takiguchi, Yoshie Imai

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Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning by Jae-Hyeon Lee, Chang-Hwan Son

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Additive Orthant Loss for Deep Face Recognition by Younghun Seo, Nam Yul Yu

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CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM by Shuqiu Tan, Jiahao Pan, Jianxun Zhang, Yahui Liu

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Aircraft Type Recognition in Remote Sensing Images: Bilinear Discriminative Extreme Learning Machine Framework by Baojun Zhao, Wei Tang, Yu Pan, Yuqi Han, Wenzheng Wang

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Extreme learning machine for joint embedding and clustering by Liu, Tianchi, Lekamalage, Chamara Kasun Liyanaarachchi, Huang, Guang-Bin, Lin, Zhiping

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An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning by Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar

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Improving Feature Learning in Remote Sensing Images Using an Integrated Deep Multi-Scale 3D/2D Convolutional Network by Haron C. Tinega, Enqing Chen, Divinah O. Nyasaka

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Improving Domain Generalization in Appearance-Based Gaze Estimation With Consistency Regularization by Moon-Ki Back, Cheol-Hwan Yoo, Jang-Hee Yoo

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A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix by Xiao Chu, Xianghua Tan, Weili Zeng

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A data-driven approach to predicting diabetes and cardiovascular disease with machine learning by An Dinh, Stacey Miertschin, Amber Young, Somya D. Mohanty

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