Cost-sensitive support vector machine using randomized dual coordinate descent method for big class-imbalanced data classification

HIGHLIGHTS

• who: Data Classification and colleagues from the School of Energy and Power Engineering, Changsha University of Science and Engineering, Changsha, China have published the research: Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification, in the Journal: (JOURNAL) of 14/04/2014
• what: The authors focus on big data class-imbalanced learning by CSVM. The authors examine large datasets with relative imbalance ratios and severe imbalance ratios to evaluate the convergence

SUMMARY

The 2C-SVM has primal optimization problem: min 1 ‖w‖2 . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or Email

Password

Remember me

Lost your password?

If you don't have an account, you can create one here.