scholarly journals Extreme Learning Machine Based on Maximum Weighted Mean Discrepancy for Unsupervised Domain Adaptation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 2283-2293
Author(s):  
Yanna Si ◽  
Jiexin Pu ◽  
Shaofei Zang ◽  
Lifan Sun
Keyword(s):  
Extreme Learning Machine ◽  
Domain Adaptation ◽  
Weighted Mean ◽  
Unsupervised Domain Adaptation ◽  
Learning Machine

Domain Space Transfer Extreme Learning Machine for Domain Adaptation

2019 ◽  
Vol 49 (5) ◽  
pp. 1909-1922 ◽  
Author(s):  
Yiming Chen ◽  
Shiji Song ◽  
Shuang Li ◽  
Le Yang ◽  
Cheng Wu
Keyword(s):  
Extreme Learning Machine ◽  
Domain Adaptation ◽  
Domain Space ◽  
Learning Machine

scholarly journals Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3643
Author(s):  
Haining Liu ◽  
Yuping Wu ◽  
Yingchang Cao ◽  
Wenjun Lv ◽  
Hongwei Han ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Sedimentary Environment ◽  
Well Logging ◽  
Learning Technologies ◽  
Bohai Bay ◽  
Learning Method ◽  
Lithology Identification ◽  
Learning Machine

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

scholarly journals Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 742 ◽  
Author(s):  
Zhiyuan Ma ◽  
Guangchun Luo ◽  
Ke Qin ◽  
Nan Wang ◽  
Weina Niu
Keyword(s):  
Extreme Learning Machine ◽  
Domain Adaptation ◽  
Online Sensor ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Learning Machine

scholarly journals Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 17029-17038 ◽  
Author(s):  
Biao Yang ◽  
Jin-Meng Cao ◽  
Nan Wang ◽  
Yu-Yu Zhang ◽  
Guo-Zeng Cui
Keyword(s):  
Extreme Learning Machine ◽  
Domain Adaptation ◽  
Learning Machine

Extreme Learning Machine-Based Traffic Incidents Detection with Domain Adaptation Transfer Learning

2017 ◽  
Vol 26 (4) ◽  
pp. 601-612
Author(s):  
Chaimae Elhatri ◽  
Mohammed Tahifa ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Incident Detection ◽  
Target Domain ◽  
Traffic Incidents ◽  
Learning Machine ◽  
Past Experiences

AbstractTraffic incidents in big cities are increasing alongside economic growth, causing traffic delays and deteriorating road safety conditions. Thus, developing a universal freeway automatic incident detection (AID) algorithm is a task that took the interest of researchers. This paper presents a novel automatic traffic incident detection method based on the extreme learning machine (ELM) algorithm. Furthermore, transfer learning has recently gained popularity as it can successfully generalise information across multiple tasks. This paper aimed to develop a new approach for the traffic domain-based domain adaptation. The ELM was used as a classifier for detection, and target domain adaptation transfer ELM (TELM-TDA) was used as a tool to transfer knowledge between environments to benefit from past experiences. The detection performance was evaluated by common criteria including detection rate, false alarm rate, and others. To prove the efficiency of the proposed method, a comparison was first made between back-propagation neural network and ELM; then, another comparison was made between ELM and TELM-TDA.

scholarly journals Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Zhiyuan Ma ◽  
Guangchun Luo ◽  
Ke Qin ◽  
Nan Wang ◽  
Weina Niu
Keyword(s):  
Extreme Learning Machine ◽  
Sensor Array ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Time Cost ◽  
Target Domain ◽  
Extended Target ◽  
Drift Compensation ◽  
Learning Machine ◽  
Domain Transfer

Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

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