[1]Mitchell T. Machine learning[M]. Beijing: China Machine Press,2003.
[2]Ganesan K,Acharya U R,Chua C K,et al.Computeraided breast cancer detection using
mammograms:A review[J].IEEE Reviews in Biomedical Engineering,2013,6(77):98.
[3]Orozco H M,Villegas O V,Maynez L O,et al.Lung nodule classification in frequency
domain using support vector machines[C]∥
Proc of 2012 11th International Conference on Information Science,Signal Processing and
their Applications (ISSPA),2012:870875.
[4]Pinheiro F M R,Kuo M H.Poster:Applying data mining algorithms to early detection of
liver cancer[C]∥
Proc of 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical
Sciences (ICCABS),2012:1.
[5]Pei Chengdan,Xu Shengzhou.Segmentation of mammography images based on the label
controlling watershed algorithm[J].Science Technology and Engineering,2013,13(5):1210
1214.(in Chinese)
[6]Wang Zhiqiong,Kang Yan,Yu Ge,et al.Breast tumor detection algorithm based on feature
selection ELM[J].Journal of Northeastern University(Natural Science),2013,34(6):792
796.(in Chinese)
[7]Japkowicz N.Learning from imbalanced data sets:A comparison of various strategies[C]
∥Proc of AAAI’2000 Workshop on Learning from Imbalanced Data Sets,2000:1015.
[8]Tan P N,Steinbach M,Kumar V.Introduction to data mining[M].Beijing:Posts & Telecom
Press,2006:426.
[9]Duda R O.Pattern recognition[M].2nd edition.Beijing:China Machine Press,2004.
[10]Liu X M,Tang J S.Mass classification in mammograms using selected geometry and
texture features,and a new SVMbased feature selection method[J].IEEE Systems
Journal,2014,8(3):910920.
[11]Li Zhenxiang,Wang Wenjian,Guo Husheng, et al.SVM classification algorithm for
solving multiclass imbalance data[J].Computer Engineering and Design,2014,35
(7):24992503.(in Chinese)
[12]Zhang Zheng,Wang Yanping,Xue Guixiang.Digital image processing and machine
vision:Implemented by Visual C++ and Matlab[M].Beijing:Posts & Telecom Press,2010.(in
Chinese)
[13]Harrington P.Machine learning in action[M].Beijing:Posts and Telecom Press,2013.
[14]Wang Zhihui.Research on BP neural networks and ELM algorithms[D].Hangzhou:China
Jiliang University,2012.(in Chinese)
[15]Haykin S.Neural networks and learning machines[M].third Edition. Beijing:China
Machine Press,2009.
[16]Yang Xiaofan,Chen Tinghuai.Advantages and disadvantages of artificial neural
networks[J].Computer Science,1994,21(2):2326.(in Chinese)
[17]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):513529.
[18]Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:A new learning scheme of
feedforward neural networks[C]∥
Proc of 2004 IEEE International Joint Conference on Neural Networks,
2004:2529.
[19]Menaka K,Karpagavalli S.Mammogram classification using extreme learning machine and
genetic programming[C]∥Proc of 2014 International Conference on Computer Communication
and Informatics (ICCCI),2014:17.
[20]Heath M,Bowyer K,Kopans D,et al.The digital databasefor screening mammography[C]∥
Proc of the 5th International Workshop on Digital Mammography,2000:212218.
[21]Cascio D,Fauci F,Magro R,et al.Mammogram segmentation by contour searching and mass
lesions classification with neural network [J].IEEE Transactions on Nuclear Science,
2006,53(5):28272833.
[22]Pradeep N,Girisha H,Sreepathi B,et al.Feature extraction of mammograms
[J].International Journal of Bioinformatics Research,
2012,4(1):241244.
[23]Lichman M. UCI machine learning repository [DB/OL].[201507
01].http://archive.ics.uci.edu/ml.Irvine.
[24]Chang C C,Lin C J.LIBSVM:A library for support vector machines[J].ACM Transactions
on Intelligent Systems and Technology,
2011,2(23):127.
附中文参考文献:
[5]裴承丹,徐胜舟.基于标记控制分水岭算法的乳腺X线摄片分割[J].科学技术与工程,2013,13(
5):12101214.
[6]王之琼,康雁,于戈,等.基于特征选择ELM的乳腺肿块检测算法[J].东北大学学报(自然科学版
),2013,34(6):792796.
[11]李珍香,王文剑,郭虎升,等.处理多类不平衡数据的SVM分类算法[J].计算机工程与设计,2014
,35(7):24992503.
[12]张铮,王艳平,薛桂香.数字图像处理与机器视觉:Visual C++与Matlab处理[M].北京:人民邮
电出版社,2010.
[14]王智慧.BP神经网络和ELM算法研究[D].杭州:中国计量学院,2012.
[16]杨晓帆,陈廷槐.人工神经网络固有的优点和缺点[J].计算机科学,1994,21(2):2326.
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