Sampling adaptively using the massart inequality for scalable learning
Document Type
Conference Proceeding
Publication Date
1-1-2013
Abstract
With the advent of the 'big data' era, the data mining community is facing an increasingly critical problem of developing scalable algorithms capable of mining knowledge from massive amount of data. This paper develops a sampling-based method to address the issue of scalability. We show how to utilize the new, adaptive sampling method in [4] to develop a scalable learning algorithm by boosting, an ensemble learning method. We present experimental results using bench-mark data sets from the UC-Irvine ML data repository that confirm the much improved efficiency and thus scalability, and competitive prediction accuracy of the new adaptive boosting method, in comparison with existing approaches. © 2013 IEEE.
Publication Source (Journal or Book title)
Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
First Page
362
Last Page
367
Recommended Citation
Chen, J., & Xu, J. (2013). Sampling adaptively using the massart inequality for scalable learning. Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, 2, 362-367. https://doi.org/10.1109/ICMLA.2013.149