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Perturbation regulated kernel regressors for supervised machine learning
S. Y. Kung
, Pei Yuan Wu
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Research output
:
Chapter in Book/Report/Conference proceeding
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Conference contribution
1
Scopus citations
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Dive into the research topics of 'Perturbation regulated kernel regressors for supervised machine learning'. Together they form a unique fingerprint.
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Keyphrases
Regressor
100%
Supervised Machine Learning
100%
Kernel Regressor
100%
Kernel Perturbations
100%
Orthogonal Polynomials
66%
Order Errors
33%
Linear Combination
33%
Measurement Error
33%
Estimation Task
33%
Estimation Error
33%
Non-Gaussian
33%
Classification Results
33%
Optimal Estimation
33%
Error Analysis
33%
Error Equation
33%
Form Error
33%
Hermite
33%
Projection Analysis
33%
Projection Theorem
33%
MSE Reduction
33%
Errors-in-variables Model
33%
Ridge Regression Model
33%
Regressor Models
33%
Regression Results
33%
Computer Science
Machine Learning
100%
Estimation Error
100%
Linear Combination
100%
Regression Method
100%
Ridge Regression
100%
classification result
100%
Variable Model
100%
Error Variable
100%
Engineering
Regressors
100%
Gaussian Case
40%
Tasks
20%
Simulation Result
20%
Gaussians
20%
Closed Form
20%
Linear Combination
20%
Estimation Error
20%
Form Error
20%
Economics, Econometrics and Finance
Machine Learning
100%