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» Learning to learn with the informative vector machine
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ICML
2004
IEEE
16 years 5 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu
JCDL
2003
ACM
160views Education» more  JCDL 2003»
15 years 9 months ago
Automatic Document Metadata Extraction Using Support Vector Machines
Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadat...
Hui Han, C. Lee Giles, Eren Manavoglu, Hongyuan Zh...
ML
2002
ACM
220views Machine Learning» more  ML 2002»
15 years 4 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
CORR
2011
Springer
163views Education» more  CORR 2011»
14 years 8 months ago
Suboptimal Solution Path Algorithm for Support Vector Machine
We consider a suboptimal solution path algorithm for the Support Vector Machine. The solution path algorithm is an effective tool for solving a sequence of a parametrized optimiz...
Masayuki Karasuyama, Ichiro Takeuchi
ICMCS
2000
IEEE
170views Multimedia» more  ICMCS 2000»
15 years 8 months ago
Update Relevant Image Weights for Content-Based Image Retrieval using Support Vector Machines
Relevance feedback [1] has been a powerful tool for interactive Content-Based Image Retrieval (CBIR). During the retrieval process, the user selects the most relevant images and p...
Qi Tian, Pengyu Hong, Thomas S. Huang