Machine learning and intelligence science: IScIDE (C)

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References

  1. 1.
    Xu L, Li Y D. Machine learning and intelligence science: Sino-foreign interchange workshop IScIDE2010 (A). Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 1–5CrossRefGoogle Scholar
  2. 2.
    Wang P H, Shi L, Du L, Liu H W, Xu L, Bao Z. Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 300–317CrossRefGoogle Scholar
  3. 3.
    Mu C X, Sun C Y. Data-based intelligent modeling and control for nonlinear systems. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 291–299CrossRefGoogle Scholar
  4. 4.
    Ye C, Liu L X, Wang X, Zhang X G. Observations on potential novel transcripts from RNA-Seq data. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 275–282CrossRefGoogle Scholar
  5. 5.
    Hu D W, Zhou Z T, Wang Z Z. Processing real-world imagery with FACADE-based approaches. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 120–136CrossRefGoogle Scholar
  6. 6.
    Yuille A. An information theory perspective on computational vision. Frontiers of Electrical and Electronic Engineering in China, 2010, 5(3): 329–346MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zhou L, Hu D W, Zhou Z T, Zhuang Z W. Natural scene recognition using weighted histograms of gradient orientation descriptor. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 318–327CrossRefGoogle Scholar
  8. 8.
    Pang Z H, Tu S K, Su D, Wu X H, Xu L. Discriminative training of GMM-HMM acoustic model by RPCL learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 283–290CrossRefGoogle Scholar
  9. 9.
    Xu L. Independent subspaces. In: Ramón J, Dopico R, Dorado J, Pazos A, eds. Encyclopedia of Artificial Intelligence. Hershey, PA: IGI Global, 2008, 903–912Google Scholar
  10. 10.
    Yin H. Advances in adaptive nonlinear manifolds and dimensionality reduction. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 72–85CrossRefGoogle Scholar
  11. 11.
    He X F, Lin B B. Tangent space learning and generalization. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 27–42CrossRefGoogle Scholar
  12. 12.
    Yang J. Kernel feature extraction methods observed from the viewpoint of generating-kernels. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 43–55CrossRefGoogle Scholar
  13. 13.
    Xu L. Codimensional matrix pairing perspective of BYY harmony learning: Hierarchy of bilinear systems, joint decomposition of data-covariance, and applications of network biology. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 86–119CrossRefGoogle Scholar
  14. 14.
    Tu S K, Xu L. Parameterizations make different model selections: Empirical findings from factor analysis. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 256–274MathSciNetCrossRefGoogle Scholar
  15. 15.
    Tu S K, Xu L. An investigation of several typical model selection criteria for detecting the number of signals. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 245–255MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zhang L J, Chen Z G, Zheng M, He X F. Robust non-negative matrix factorization. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 192–200CrossRefGoogle Scholar
  17. 17.
    Zhou Z H. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6–16CrossRefGoogle Scholar
  18. 18.
    Zhang C S, Wang F. Graph-based semi-supervised learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 17–26CrossRefGoogle Scholar
  19. 19.
    Lu B L, Wang X L, Yang Y, Zhao H. Learning from imbalanced data sets with a Min-Max modular support vector machine. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 56–71CrossRefGoogle Scholar
  20. 20.
    Xu L, Amari S. Combining classifiers and learning mixture-of-experts. In: Dopico J R R, Dorado J, Pazos A, eds. Encyclopedia of Artificial Intelligence. 2009, 318–326Google Scholar
  21. 21.
    Xu L. Bayesian Ying-Yang system, best harmony learning, and five action circling. Frontiers of Electrical and Electronic Engineering in China, 2010, 5(3): 281–328CrossRefGoogle Scholar
  22. 22.
    Liao L, Zhang Y N. MRI image segmentation based on fast kernel clustering analysis. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 363–373CrossRefGoogle Scholar
  23. 23.
    Shi L, Tu S K, Xu L. Learning Gaussian mixture with automatic model selection: A comparative study on three Bayesian related approaches. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 215–244CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
  2. 2.Department of AutomationTsinghua UniversityBeijingChina

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