|Table of Contents|

A Novel Extreme Learning Machine Based on Bagged Kernel(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
145-150
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
A Novel Extreme Learning Machine Based on Bagged Kernel
Author(s):
Wang Lijuan12Ding Shifei1
(1.School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China)(2.School of Information and Electrical Engineering,Xuzhou College of Industrial Technology,Xuzhou 221140,China)
Keywords:
ELMk-means clusteringBagged kernelRBF kernel function
PACS:
TP3
DOI:
10.3969/j.issn.1001-4616.2019.03.019
Abstract:
The traditional neural network learning algorithm(BP algorithm)need to set a large amount of network training parameter,and prone to local optimal solution. Extreme learning machine(ELM)need to set the number of hidden layer nodes of networks,while execution of the algorithm does not need to adjust the network weights of the input and hidden element bias,and can produce the optimal solution,thus it has advantages of fast learning speed and good generalization capability. Extreme learning machine as a kind of machine learning method,with simple and easy to use,and effective single hidden layer feed forward neural network learning algorithm,caught the attention of more and more researchers. With the research and development of extreme learning machine,the theory of nuclear extreme learning machine has been continually raised. Nuclear ultimate learning machine is introduced to limit the kernel learning machine,with which you can get a least-squares optimization solution,a more stable,better generalization performance. We now put forward a novel extreme learning machine based on bagged kernel classification method. First of all,the existing tag samples and all unmarked samples use k-means clustering algorithm for many times to construct the bagged clustering nucleus. Then,bagged clustering nucleus and the radial basis calculate the sum,and eventually it is used in training and classification of extreme learning machine. Compared with the traditional extreme learning machine,the new algorithm can use all unmarked sample information,as much as possible to improve the classification accuracy,and further improve the running speed. Through the experimental data set,we verify the feasibility of the method.

References:

[1] HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:a new learning scheme of feed forward neural networks[C]//International Joint Conference on Neural Networks. Budapest,2004:985-990.
[2]HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing,2006,70:489-501.
[3]VAPNIK V N. The nature of statistical learning theory[M]. New York:Springer,1995.
[4]孔怡青. 半监督学习及其应用研究[D]. 无锡:江南大学,2009.
[5]李小冬. 核极速学习机的理论与算法及其在图像处理中的应用[D]. 杭州:浙江大学,2014.
[6]邓万宇,郑庆华,陈琳,等. 神经网络极速学习方法研究[J]. 计算机学报,2010,33(2):279-287.
[7]ZHU Q Y,QIN A K,SUGANTHAN P N,et al. Evolutionary extreme learning machine[J]. Pattern recognition,2005,38(10):1759-1763.
[8]HUANG G B,ZHOU H M,DING X J,et al. Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems,man,and cybernetics,part B:cybernetics,2012,42(2):513-529.
[9]HUANG G B,SIEW C K. Extreme learning machine with randomly assigned RBF kernels[J]. International journal of information technology,2005,11(1):16-24.
[10]丁世飞. 孪生支持向量机:算法、理论与拓展[M]. 北京:科学出版社,2017.
[11]Lü F,HAN M. Hyperspectral image classification based on multiple reduced kernel extreme learning machine[J]. International journal of machine learning and cybernetics,2019,https://doi.org/10.1007/s13042-019-00926-5.
[12]ZHANG J,DING S F,ZHANG N,et al. An incremental extreme learning machine based on deep feature embedded[J]. International journal of machine learning and cybernetics,2016,7(1):111-120.
[13]梁吉业,高嘉伟,常瑜. 半监督学习研究进展[J]. 山西大学学报(自然科学版),2009,32(4):528-534.
[14]DING S F,ZHANG J,XU X Z,et al. A wavelet extreme learning machine[J]. Neural computing and applications,2016,27(4):1033-1040.
[15]WANG H B,LIU X,SONG P,et al. Sensitive time series prediction using extreme learning machine[J]. International journal of machine learning and cybernetics,2019.(https://doi.org/10.1007/s13042-019-00924-7)
[16]WESTON J,LESLIE C,ZHOU D,et al. Semi-supervised protein classification using cluster kernels[C]//Advances in Neural Information Processing System 17(NIPS 2004). Vancouver,2004:595-602.
[17]DING S F,ZHANGY N,XU X Z. A novel extreme learning machine based on hybrid kernel function[J]. Journal of computers,2013,8(8):2110-2117.
[18]HUANG G,HUANG G B,SONG S G,et al. Trends in extreme learning machines:a review[J]. Neural networks,2015,61:32-48.
[19]HUANG G,SONG S G,JATINDER N D. Semi-supervised and unsupervised extreme learning machines[J]. IEEE transactions on cybernetics,2014,44(12):2405-2417.
[20]CHAPELLE O,WESTON J,SCHOLKOPF B. Cluster kernels for semi-supervised learning[C]//Advances in Neural Information Processing System 16(NIPS 2003). Vancouver,2003:601-608.

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Last Update: 2019-09-30