Imaging and representation learning of solar radio spectrums for classification

Multimedia Tools and Applications

Volumn 75, Issue 5, 2016, Pages 2859-2875

  Chen, Zhuo ^a   Ma, Lin ^b   Xu, Long ^a   Tan, Chengming ^a   Yan, Yihua ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b HUAWEI NOAH S ARK LAB   (China)

Author keywords

Classification; Deep learning; Feature learning; Solar radio astronomy

Indexed keywords

ASTRONOMY; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; RADIO ASTRONOMY;

CLASSIFICATION ACCURACY; DEEP LEARNING; FEATURE LEARNING; RADIO IMAGES; RADIO SPECTRA; SOLAR IMAGES; SOLAR RADIO ASTRONOMY; SOLAR RADIO BURSTS;

RADIO COMMUNICATION;

EID: 84959904306     PISSN: 13807501     EISSN: 15737721     Source Type: Journal    
DOI: 10.1007/s11042-015-2528-2     Document Type: Article

References (17)

1. Bengio Y (2009) Learning deep architectures for AI. FTML 2(1):1–127
2. Chen M, Weinberger K, Sha F, Bengio Y (2014) Marginalized denoising autoencoders for nonlinear representation. ICML
3. Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized stacked denoising autoencoders for domain adaptation 29th International Conference on Machine Learning (ICML)
4. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. JMLR 12:2493–2537
5. Deng L (2012) Three classes of deep learning architectures and their applications: a tutorial survey, APSIPA transactions on signal and information processing
6. Dong J, Ngan KN, Fong CK et al (2009) 2-D order-16 integer transforms for HD video coding. IEEE Trans Circuits Syst Video Technol 19(10):1462–1474
7. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874
8. Fu Q, Ji H, Qin Z et al (2004) A new solar broadband radio spectrometer (SBRS) in China. Solar Phys 222(1):167–173
9. Hinton GE (2010) A practical guide to training restricted boltzmann machines. Technical report, University of Toronto
10. Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554
11. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
12. Le Q, Ranzato M, Monga R, Devin M, Chen K, Corrado G, Dean J, Ng A (2012) Building high-level features using large scale unsupervised learning. ICML
13. Lee H, Largman Y, Pham P, Ng AY (2009) Unsupervised feature learning for audio classification using convolutional deep belief networks. NIPS
14. Mohamed AR, Dahl G, Hinton GE (2012) Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 20(1):14–22
15. Papoulis A, Pillai SU (2002) Probability, random variables, and stochastic processes. McGraw-Hill, Boston
16. Salakhutdinov R, Murray I (2008) On the quantitative analysis of deep belief networks. ICML
17. Sohn K, Jung DY, Lee H, Hero A (2011) Efficient learning of sparse, distributed, convolutional feature representations for object recognition. ICCV