Searching for authors named Vladimir Cherkassky – sorted by Relevance.
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Inductive Principles for Learning from Data
- Inductive Principles for Learning from Data Vladimir Cherkassky Department of Electrical
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Multiple Model Estimation: A New Formulation for Predictive Learning. under review
- Multiple Model Estimation: A New Formulation for Predictive Learning Abstract Vladimir Cherkassky
- Cited by 2 (0 self) – Add To MetaCart
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Practical selection of svm parameters and noise estimation for svm regression
- Practical Selection of SVM Parameters and Noise Estimation for SVM Regression Vladimir Cherkassky
- Cited by 19 (0 self) – Add To MetaCart
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Digital Images Digital Compression
- . Author(s) 8. Performing Organization Report No. Vladimir Cherkassky, Xuhao He, Jie Shao 9. Performing
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Measuring The VC-dimension Using Optimized Experimental Design
- 1 Measuring The VC-dimension Using Optimized Experimental Design Xuhui Shao, Vladimir Cherkassky
- Cited by 4 (0 self) – Add To MetaCart
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Determining The Skeletal Description Of Sparse Shapes
- Determining The Skeletal Description Of Sparse Shapes Rahul Singh y Vladimir Cherkassky z
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Self-Organizing Maps for the Skeletonization of Sparse Shapes
- Shapes Rahul Singh y , Vladimir Cherkassky z , Nikolaos P. Papanikolopoulos y y Artificial
- Cited by 6 (0 self) – Add To MetaCart
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Object Skeletons From Sparse Shapes In Industrial Image Settings
- . Papanikolopoulos y Vladimir Cherkassky z yArtificial Intelligence, Robotics, and Vision Laboratory Department
- Cited by 1 (0 self) – Add To MetaCart
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V.: Motion estimation using statistical learning theory
- Member, IEEE, Fayin Li, and Vladimir Cherkassky, Senior Member, IEEE Abstract—This paper describes a
- Cited by 2 (0 self) – Add To MetaCart
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Efficient model selection for regularized linear discriminant analysis
- , MN 55455 janardan@(email omitted); Vladimir Cherkassky University of Minnesota Minneapolis, MN 55455
- Cited by 1 (0 self) – Add To MetaCart

