scholarly journals Online transfer learning with extreme learning machine

2017 ◽  
Author(s):  
Haibo Yin ◽  
Yun-an Yang
Keyword(s):  
Extreme Learning Machine ◽  
Transfer Learning ◽  
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.

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.

Extreme learning machine based transfer learning algorithms: A survey

2017 ◽  
Vol 267 ◽  
pp. 516-524 ◽  
Author(s):  
Syed Moshfeq Salaken ◽  
Abbas Khosravi ◽  
Thanh Nguyen ◽  
Saeid Nahavandi
Keyword(s):  
Extreme Learning Machine ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Learning Machine

scholarly journals Transfer Extreme Learning Machine with Output Weight Alignment

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shaofei Zang ◽  
Yuhu Cheng ◽  
Xuesong Wang ◽  
Yongyi Yan
Keyword(s):  
Objective Function ◽  
Extreme Learning Machine ◽  
Transfer Learning ◽  
Training Sample ◽  
Least Square ◽  
Weight Matrix ◽  
Transfer Of Knowledge ◽  
Cross Domain ◽  
Learning Machine ◽  
Domain Transfer

Extreme Learning Machine (ELM) as a fast and efficient neural network model in pattern recognition and machine learning will decline when the labeled training sample is insufficient. Transfer learning helps the target task to learn a reliable model by using plentiful labeled samples from the different but relevant domain. In this paper, we propose a supervised Extreme Learning Machine with knowledge transferability, called Transfer Extreme Learning Machine with Output Weight Alignment (TELM-OWA). Firstly, it reduces the distribution difference between domains by aligning the output weight matrix of the ELM trained by the labeled samples from the source and target domains. Secondly, the approximation between the interdomain ELM output weight matrix is added to the objective function to further realize the cross-domain transfer of knowledge. Thirdly, we consider the objective function as the least square problem and transform it into a standard ELM model to be efficiently solved. Finally, the effectiveness of the proposed algorithm is verified by classification experiments on 16 sets of image datasets and 6 sets of text datasets, and the result demonstrates the competitive performance of our method with respect to other ELM models and transfer learning approach.

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