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Multiple Model Classification Using SVM-based Approach
Yunqian Ma,
Vladimir S Cherkassky
Electrical and Computer Engineering
Research output
:
Contribution to conference
›
Paper
›
peer-review
9
Scopus citations
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Dive into the research topics of 'Multiple Model Classification Using SVM-based Approach'. Together they form a unique fingerprint.
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Keyphrases
Classification Accuracy
33%
Classification Methods
66%
Classification Model
100%
Component Model
33%
Goods Classification
33%
Heuristic Tuning
33%
Kernel Type
33%
Learning Problems
33%
Linear Classifier
33%
Linear Components
33%
Multiple Model Adaptive Estimation
33%
Multiple Models
100%
Non-linear SVM Classifier
33%
Nonlinear Classification
33%
Nonlinear SVM
33%
Practical Implementation
33%
Prediction Accuracy
33%
Regularization Parameter
33%
SVM Classification Algorithm
33%
SVM Parameters
33%
SVM-based
100%
Computer Science
Classification Accuracy
25%
Classification Algorithm
25%
Classification Method
50%
Classification Models
100%
Component Model
25%
Learning Problem
25%
Linear Classifier
25%
Linear Component
25%
Prediction Accuracy
25%
Regularization Parameter
25%
Support Vector Machine
100%
Mathematics
Classification Method
50%
Multiple Model
100%
Practical Advantage
25%
Regularization
25%