Open Access
| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 7 | |
| Section | Session 1: Robotics | |
| DOI | https://doi.org/10.1051/itmconf/20171201024 | |
| Published online | 05 September 2017 | |
- Tipping, Michael E. “Sparse bayesian learning and the relevance vector machine.” Journal of Machine Learning Research 1.3(2001): 211–244. [MathSciNet] [Google Scholar]
- Vapnik, Vladimir N. The Nature of Statistical Learning Theory. The nature of statistical learning theory /. Springer, 2000:1564–1564. [Google Scholar]
- Gupta, Rishabh, K. U. R.Laghari, and T. H. Falk. “Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization.” Neurocomputing 174(2015):875–884. [CrossRef] [Google Scholar]
- Zhou, Yan, et al. “Deception detecting from speech signal using relevance vector machine and non-linear dynamics features.” Neurocomputing 151(2015):1042–1052. [CrossRef] [Google Scholar]
- Tipping, Michael E, and A. C. Faul. “Fast Marginal Likelihood Maximisation for Sparse Bayesian Models.” International Workshop on Artificial Intelligence and Statistics 2003. [Google Scholar]
- Silva, C, and B. Ribeiro,. “Towards expanding relevance vector machines to large scale datasets.” International Journal of Neural Systems 18.1(2008):45–58. [CrossRef] [Google Scholar]
- Yang, Depeng, et al. “High Performance Relevance Vector Machine on GPUs.” Symposium on Application Accelerators in High PERFORMANCE Computing 2010. [Google Scholar]
- Dimitris Tzikas, Aristidis Likas, and Nikolas Galatsanos. “Large Scale Multikernel Relevance Vector Machine for Object Detection.” International Journal of Artificial Intelligence Tools 16.6(2011):967–979. [Google Scholar]
- Dong, et al. “Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing.” Mathematical Problems in Engineering 2012. 1024-123X(2012):865–883. [Google Scholar]
- Zaharia, Matei, et al. “Spark: Cluster Computing with Working Sets.” (2010):10–10. [Google Scholar]
- Friedman, Jerome, T. Hastie, and R. Tibshirani. “Additive logistic regression: a statistical view of boosting.” Annals of Statistics 28.2(2000):págs. 374–376. [Google Scholar]
- Dean, Jeffrey, and S. Ghemawat. “MapReduce: Simplified Data Processing on Large Clusters.” Conference on Symposium on Opearting Systems Design & Implementation 2004:107–113. [Google Scholar]
- Valiant, L. G. “A theory of the learnable.” Communications of the Acm 27.11(1984):1134–1142. [Google Scholar]
- P. Ithaya Rani, and K. Muneeswaran. “Facial Emotion Recognition Based on Eye and Mouth Regions.” International Journal of Pattern Recognition & Artificial Intelligence 30.07(2016). [Google Scholar]
- Zhuang, Weiwei, Q. Jiang, and T. Xiong. “An Intelligent Anti-phishing Strategy Model for Phishing Website Detection.” International Conference on Distributed Computing Systems Workshops IEEE Computer Society, 2012:51–56. [Google Scholar]
- Chawla, Nitesh V., et al. “SMOTE: synthetic minority over-sampling technique.” Journal of Artificial Intelligence Research 16.1(2002):321–357. [Google Scholar]
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