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FOCM
2006
97views more  FOCM 2006»
14 years 9 months ago
Learning Rates of Least-Square Regularized Regression
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
WSCG
2004
166views more  WSCG 2004»
14 years 10 months ago
De-noising and Recovering Images Based on Kernel PCA Theory
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
Pengcheng Xi, Tao Xu
ICANN
1997
Springer
15 years 1 months ago
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
ICIP
2001
IEEE
15 years 11 months ago
Study of embedded font context and kernel space methods for improved videotext recognition
Videotext refers to text superimposed on video frames. A videotext based Multimedia Description Scheme has recently been adopted into the MPEG-7 standard. A study of published wor...
Chitra Dorai, Hrishikesh Aradhye, Jae-Chang Shim
AAAI
2007
14 years 11 months ago
A Randomized String Kernel and Its Application to RNA Interference
String kernels directly model sequence similarities without the necessity of extracting numerical features in a vector space. Since they better capture complex traits in the seque...
Shibin Qiu, Terran Lane, Ljubomir J. Buturovic