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PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 2 months ago
Sparse Kernel SVMs via Cutting-Plane Training
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
Thorsten Joachims, Chun-Nam John Yu
ICPR
2008
IEEE
14 years 2 months ago
Text detection from scene images using sparse representation
A sparse representation based method is proposed for text detection from scene images. We start with edge information extracted using Canny operator and then group these edge poin...
Wumo Pan, Tien D. Bui, Ching Y. Suen
ACMSE
2006
ACM
14 years 2 months ago
Support vector machines for collaborative filtering
Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text ca...
Zhonghang Xia, Yulin Dong, Guangming Xing
ICMLA
2008
13 years 9 months ago
Data Integration for Recommendation Systems
The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, ...
Zhonghang Xia, Houduo Qi, Manghui Tu, Wenke Zhang
NIPS
2008
13 years 9 months ago
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung