论文信息 - Support vector machines and kernel methods: new approaches in unsupervised learning - 字舞流文

Support vector machines and kernel methods: new approaches in unsupervised learning

Johan A. K. Suykens | Carlos Alzate | J. Suykens | C. Alzate

[1] G. Wahba. Spline models for observational data , 1990 .

[2] Alexander Zien,et al. Semi-Supervised Learning , 2006 .

[3] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[4] Felipe Cucker,et al. Learning Theory: An Approximation Theory Viewpoint: Index , 2007 .

[5] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[6] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .

[7] Felipe Cucker,et al. Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics) , 2007 .

[8] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.

[9] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .

[10] Vladimir Vapnik,et al. Statistical learning theory , 1998 .