원문정보
초록
영어
Machine learning is ideal for exploiting the opportunities hidden in big data. It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. In this paper we propose a robust machine learning approach for dealing with large data set. Through experimental results, proposed method performs well on large data sets.
목차
1. Introduction
2. Incremental KPCA
2.1. Eigenspace Updating Criterion
3. SVM and LS-SVM
3.1. Support Vector Machine
3.2. LS -SVM for Big Data
4. Experiment
4.1. YouTube Comedy Slam Preference Data Set
4.2. KDD CUP 99 Data
4.3. Wine Data
4.4. NIST Handwritten Data Set
4.5. Comparison with SVM
5. Conclusion and Remarks
Acknowledgment
References