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Clustered support vector machines
Quanquan Gu,
Jiawei Han
Information Trust Institute
Carl R. Woese Institute for Genomic Biology
Siebel School of Computing and Data Science
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Keyphrases
Support Vector Machine
100%
Kernel Support Vector Machine
66%
Linear SVM
50%
Weight Vector
33%
Global Regularization
33%
Divide-and-conquer
16%
Performance Prediction
16%
Benchmark Dataset
16%
Local Linear
16%
Computational Burden
16%
Data Dependency
16%
Overfitting
16%
Machine Learning
16%
Locally Linear
16%
Linear Classifier
16%
Large-scale Dataset
16%
Linear Support Vector Machine
16%
Generalization Error Bound
16%
Global Weight
16%
Nonlinear Classifier
16%
Computer Science
Support Vector Machine
100%
Support Vector Machine
27%
Regularization
18%
Machine Learning
9%
Prediction Performance
9%
Linear Classifier
9%
Generalization Error
9%
Mathematics
Support Vector Machine
100%
Regularization
18%
Divide and Conquer
9%
Error Bound
9%
Dependent Data
9%