SVM Tutorial - Classification, Regression and Ranking

@inproceedings{Yu2012SVMT,
  title={SVM Tutorial - Classification, Regression and Ranking},
  author={Hwanjo Yu and Sungchul Kim},
  booktitle={Handbook of Natural Computing},
  year={2012}
}
Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. Two special properties of SVMs are that SVMs achieve (1) high generalization by maximizing the margin… Expand
Ranking Support Vector Machine with Kernel Approximation
Step Function Approximation for Support Vector Reduction
A Comparative Analysis of Machine Learning Classifiers for Twitter Sentiment Analysis
A ranking SVM based fusion model for cross-media meta-search engine
Classification- and Regression-Assisted Differential Evolution for Computationally Expensive Problems
A Fast and Robust Negative Mining Approach for Enrollment in Face Recognition Systems
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 45 REFERENCES
SVM selective sampling for ranking with application to data retrieval
Training linear SVMs in linear time
Feature Selection for Nonlinear Kernel Support Vector Machines
  • O. Mangasarian, Gang Kou
  • Computer Science
  • Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)
  • 2007
AdaRank: a boosting algorithm for information retrieval
Adapting ranking SVM to document retrieval
Learning to rank for information retrieval
A Feature Selection Newton Method for Support Vector Machine Classification
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Feature selection in a kernel space
...
1
2
3
4
5
...