Learning with information of features 2009-06-05 panec
LOGO Learning with information of features 2009-06-05
http:/parnec.nuaa.edu.cn Contents Motivation Incorporating prior knowledge on features into learning (AISTATS OT Regularized learning with networks of features (NIPS08 Conclusion
Company name www.themegallery.com Contents Motivation Regularized learning with networks of features (NIPS’08) Incorporating prior knowledge on features into learning (AISTATS’07) Conclusion
http:/parnec.nuaa.edu.cn Motivation Given data X∈R×dx1 2d n min ∑。1(,f(x)+f IF prior information of samples Manifold structure information LAPSVM Transformation invariance VSVM. ISSVM Permutation invariance 丌-SVM Imbalance information SVM for imbalance distribution Cluster structure information Structure sⅤM
Company name www.themegallery.com Motivation 11 12 1 21 22 2 1 2 d d n n nd n d x x x x x x x x x Given data X∈R n×d n i=1 min ( , ( )) || || i i F l y f x f + + prior information of samples Manifold structure information LAPSVM Transformation invariance VSVM, ISSVM Permutation invariance π- SVM Imbalance information SVM for imbalance distribution Cluster structure information Structure SVM
http:/parnec.nuaa.edu.cn Motivation 12x 2d n2 Information in the sample spa ace (Space spanned by samples)
Company name www.themegallery.com Motivation 11 12 1 21 22 2 1 2 d d n n nd n d x x x x x x x x x Information in the sample space (space spanned by samples)
http:/parnec.nuaa.edu.cn Motivation 12 Prior information in the feature or attribute space (Space spanned by features)
Company name www.themegallery.com Motivation 11 12 1 21 22 2 1 2 d d n n nd n d x x x x x x x x x Prior information in the feature or attribute space (space spanned by features)