Machine learning and intelligence science: IScIDE (C)

doi:10.1007/s11460-012-0194-y

Front Elect Electr Eng

2012, Vol. 7

Issue (1) : 1-4 https://doi.org/10.1007/s11460-012-0194-y

EDITORIAL

Machine learning and intelligence science: IScIDE (C)

Lei XU¹(

), Yanda LI²(

)

1. Department of Computer Science and Engineering, The Chinese Unversity of Hong Kong, Hong Kong, China; 2. Department of Automation, Tsinghua University, Beijing 100084, China

Download: PDF(99 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Corresponding Author(s): XU Lei,Email:lxu@cse.cuhk.edu.hk; LI Yanda,Email:daulyd@tsinghua.edu.cn

Issue Date: 05 March 2012

Cite this article:

Lei XU,Yanda LI. Machine learning and intelligence science: IScIDE (C)[J]. Front Elect Electr Eng, 2012, 7(1): 1-4.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-012-0194-y
https://academic.hep.com.cn/fee/EN/Y2012/V7/I1/1

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): 1-5 doi: 10.1007/s11460-011-0136-0
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): 300-317 doi: 10.1007/s11460-011-0149-8
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): 291-299 doi: 10.1007/s11460-011-0143-1
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): 275-282 doi: 10.1007/s11460-011-0148-9
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): 120-136 doi: 10.1007/s11460-011-0133-3
6	Yuille A. An information theory perspective on computational vision. Frontiers of Electrical and Electronic Engineering in China , 2010, 5(3): 329-346 doi: 10.1007/s11460-010-0107-x
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): 318-327 doi: 10.1007/s11460-011-0140-4
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): 283-290 doi: 10.1007/s11460-011-0152-0
9	Xu L. Independent subspaces. In: Ram′on J, Dopico R, Dorado J, Pazos A, eds. Encyclopedia of Arti?cial Intelligence . Hershey, PA: IGI Global, 2008, 903-912 doi: 10.4018/978-1-59904-849-9.ch132
10	Yin H. Advances in adaptive nonlinear manifolds and dimensionality reduction. Frontiers of Electrical and Electronic Engineering in China , 2011, 6(1): 72-85 doi: 10.1007/s11460-011-0131-5
11	He X F, Lin B B. Tangent space learning and generalization. Frontiers of Electrical and Electronic Engineering in China , 2011, 6(1): 27-42 doi: 10.1007/s11460-011-0124-4
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): 43-55 doi: 10.1007/s11460-011-0129-z
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): 86-119 doi: 10.1007/s11460-011-0135-1
14	Tu S K, Xu L. Parameterizations make different model selections: Empirical ?ndings from factor analysis. Frontiers of Electrical and Electronic Engineering in China , 2011, 6(2): 256-274 doi: 10.1007/s11460-011-0150-2
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): 245-255 doi: 10.1007/s11460-011-0146-y
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): 192-200 doi: 10.1007/s11460-011-0128-0
17	Zhou Z H. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China , 2011, 6(1): 6-16 doi: 10.1007/s11460-011-0126-2
18	Zhang C S, Wang F. Graph-based semi-supervised learning. Frontiers of Electrical and Electronic Engineering in China , 2011, 6(1): 17-26 doi: 10.1007/s11460-011-0130-6
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): 56-71 doi: 10.1007/s11460-011-0127-1
20	Xu L, Amari S. Combining classi?ers and learning mixture-of-experts. In: Dopico J R R, Dorado J, Pazos A, eds. Encyclopedia of Arti?cial Intelligence . 2009, 318-326
21	Xu L. Bayesian Ying-Yang system, best harmony learning, and ?ve action circling. Frontiers of Electrical and Electronic Engineering in China , 2010, 5(3): 281-328 doi: 10.1007/s11460-010-0108-9
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): 363-373 doi: 10.1007/s11460-011-0154-y
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): 215-244 doi: 10.1007/s11460-011-0153-z

Viewed

Full text

Abstract

Cited

Shared

Discussed