Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
Motion prediction using VC-generalization bounds
Harry Wechsler
, Zoran Duric
, Li Fayin
, Vladimir S. Cherkassky
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Motion prediction using VC-generalization bounds'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Generalization Bounds
100%
Motion Prediction
100%
Statistical Learning Theory
66%
Risk Prediction
33%
Model Selection
33%
Image Sequence
33%
Generalization Performance
33%
Model Complexity
33%
Small Sets
33%
VC-dimension
33%
Image Measurement
33%
Motion Model
33%
Motion Interpolation
33%
Optimal Motion
33%
Motion Extrapolation
33%
Empirical Error
33%
Number of Training Samples
33%
Computer Science
Statistical Learning Theory
100%
Image Sequence
50%
Model Complexity
50%
Generalization Performance
50%
Motion Model
50%
Training Sample
50%
Model Selection
50%
Mathematics
Training Sample
100%
Small Set
100%
Model Selection
100%
VC Dimension
100%
Engineering
Statistical Learning Theory
100%
Flow Measurement
50%
Risk Prediction
50%
Motion Model
50%
Image Sequence
50%