Herbrich R, Graepel T, Obermayer K. Large Margin Rank Boundaries for Ordinal Regression // Bartiett P J, Schlkopf B, Schuurmans D, et al., eds. Advances in Large Margin Classifiers. Cambridge, USA: MIT Press, 2000: 115-132
[2]
Frank E, Hall M. A Simple Approach to Ordinal Classification // Proc of the 12th European Conference on Machine Learning. Freiburg, Germany, 2001: 145-156
[3]
Waegeman W, Boullart L. An Ensemble of Weighted Support Vector Machines for Ordinal Regression. International Journal of Computer Systems Science and Engineering, 2009, 3(1): 47-51
[4]
Chu W, Keerthi S S. New Approaches to Support Vector Ordinal Regression // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 145-152
[5]
Kramer S, Widmer G, Pfahringer B, et al. Prediction of Ordinal Classes Using Regression Trees // Proc of the 12th International Symposium on Methodologies for Intelligent Systems. Charlotte, USA, 2000: 426-434
[6]
Cardoso J S, da Costa J F P. Learning to Classify Ordinal Data: The Data Replication Method. Journal of Machine Learning Research, 2007, 8: 1393-1429
[7]
Lin H T, Li L. Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification. Neural Computation, 2012, 24(5): 1329-1367
[8]
Shashua A, Levin A. Ranking with Large Margin Principle: Two Approaches // Becker S, Thrun S, Obermayer K, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002: 937-944
[9]
Sun B Y, Li J Y, Wu D D, et al. Kernel Discriminant Learning for Ordinal Regression. IEEE Trans on Knowledge and Data Enginee-ring, 2010, 22(6): 906-910
[10]
Liu Y, Liu Y, Chan K C C. Ordinal Regression via Manifold Learning // Proc of the 25th AAAI Conference on Artificial Intelligence. San Francisco, USA, 2011: 398-403
[11]
Seah C W, Tsang I W, Ong Y S. Transductive Ordinal Regression. IEEE Trans on Neural Networks and Learning Systems, 2012, 23(7): 1074-1086
[12]
Peerbhay K Y, Mutanga O, Ismail R. Commercial Tree Species Discrimination Using Airborne AISA Eagle Hyperspectral Imagery and Partial Least Squares Discriminant Analysis (PLS-DA) in Kwa Zulu-Natal, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79: 19-28
[13]
Bakry A, Elgammal A. MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 684-691
[14]
Xu Y L, Chen D R, Li H X. Least Square Regularized Regression for Multitask Learning [EB/OL].[2014-02-01]. http://www.hindawi.com/journals/aas/2013/715275
[15]
Chen K, Gong S G, Xiang T, et al. Cumulative Attribute Space for Age and Crowd Density Estimation // Proc of the 26th IEEE International Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 2467-2474
[16]
Xiang S M, Nie F P, Meng G F, et al. Discriminative Least Squares Regression for Multiclass Classification and Feature Selection. IEEE Trans on Neural Networks and Learning Systems, 2012, 23(11): 1738-1754
[17]
Wu Q, Ying Y M, Zhou D X. Learning Rates of Least-Square Regularized Regression. Foundations of Computational Mathema-tics, 2006, 6(2): 171-192
[18]
Chen L, Tsang I W, Xu D. Laplacian Embedded Regression for Scalable Manifold Regularization. IEEE Trans on Neural Networks and Learning Systems, 2012, 23(6): 902-915
[19]
Vapnik V N. The Nature of Statistical Learning Theory. 2nd Edition. NewYork, USA: Springer-Verlag, 2000
[20]
Chang K Y, Chen C S, Hung Y P. Ordinal Hyperplanes Ranker with Cost Sensitivities for Age Estimation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 585-592
[21]
Zhang Y, Yeung D Y. Multi-task Warped Gaussian Process for Personalized Age Estimation // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010: 2622-2629
[22]
Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines[EB/OL].[2014-02-10]. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf