Extreme learning machine based transfer learning for data classification

Li, Xiaodong; Mao, Weijie; Jiang, Wei

doi:10.1016/j.neucom.2015.01.096

Cited by 66 publications

(26 citation statements)

References 29 publications

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“…Among the parameter transfer approaches, the majority of related works incorporated the source model information into the target by regularizing the difference of the parameters between the source and the target domain [13]- [16]. The representative method is the adaptive SVM (A-SVM) [13], which learns from the source domain parameters by directly regularizing the distance between the learned model and the target model.…”

Section: Related Workmentioning

confidence: 99%

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Parameter Transfer Extreme Learning Machine based on Projective Model

Chen

Jiang

Jin

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Recent years, transfer learning has attracted much attention in the community of machine learning. In this paper, we mainly focus on the tasks of parameter transfer under the framework of extreme learning machine (ELM). Unlike the existing parameter transfer approaches, which incorporate the source model information into the target by regularizing the difference between the source and target domain parameters, an intuitively appealing projective-model is proposed to bridge the source and target model parameters. Specifically, we formulate the parameter transfer in the ELM networks by the means of parameter projection, and train the model by optimizing the projection matrix and classifier parameters jointly. Further more, the 2,1 -norm structured sparsity penalty is imposed on the source domain parameters, which encourages the joint feature selection and parameter transfer. To evaluate the effectiveness of the proposed method, comprehensive experiments on several commonly used domain adaptation datasets are presented. The results show that the proposed method significantly outperforms the non-transfer ELM networks and other classical transfer learning methods.

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Section: Related Workmentioning

confidence: 99%

“…Apart from this, the parameter transfer approach is another highly concerned line of works. It assumes that the transferred knowledge has been encoded into the hyperparameters of the classification model [13]- [16]. Therefore, the source model and target model should share some parameters or prior distribution of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Transfer Extreme Learning Machine based on Projective Model

Chen

Jiang

Jin

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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“…It focuses on the knowledge transfer between different but similar areas, tasks and distributions. When the task from one new domain comes, new domain samples are relabeled costly, and it would be a waste to discard all the old domain data (Li et al, 2015). Wang et al (2014) propose a transfer learning method for collaborative filtering, called Feature Subspace Transfer (FST) to overcome the sparsity problem in collaborative filtering.…”

Section: Related Workmentioning

confidence: 99%

Tool selection method based on transfer learning for CNC machines

et al. 2018

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Abstract. Owing to the changes in product requirements and development of new tool technology, traditional tool selection approach based on the human experience is leading to time-consuming and low efficiency. Under the cooperation of historical data resource accumulated by manufacturing enterprises, with human expert resource, a new tool selection mechanism can be established. In this paper, we apply transfer learning to tool selection issue. Starting from the foundation of migration, we showed a unified expression of expert experience and process case in a multi-source heterogeneous environment. Then, we propose a transfer learning algorithm (TLrAdaBoost) based on AdaBoost, which uses a small amount of target domain data (expert experience sample) and a large number of source domain low-quality data (process case sample), to build a high-quality classification model. Experimental results show the effectiveness of the proposed algorithm.

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“…Transfer learning has achieved remarkable results in resisting this challenge by transferring knowledge from source to target domains with different distributions [13]. Therefore, transfer learning attracts more and more researcher attention and has made great progress: Gao et al [14] proposed a local weighted embedded transfer learning algorithm LWE; a feature-based space transfer learning method LMPROJ are proposed by Brain et al [15]; Lu et al [16] proposed a selective transfer algorithm STLCF for collaborative filtering; Long et al [17] proposed an SVM-based least squares transfer learning framework ARTL; Xie et al [18] applied transfer learning to incremental learning and proposed an STIL algorithm; Li et al [19] proposed a new transfer learning algorithm TL-DAKELM based on the extreme learning machine; Li et al [20] proposed a transfer learning algorithm, RankRE-TL.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Deep Transfer Neural Network Algorithm

Xue

et al. 2019

Sensors

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Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.

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Extreme learning machine based transfer learning for data classification

Cited by 66 publications

References 29 publications

Parameter Transfer Extreme Learning Machine based on Projective Model

Parameter Transfer Extreme Learning Machine based on Projective Model

Tool selection method based on transfer learning for CNC machines

Multi-Source Deep Transfer Neural Network Algorithm

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