SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don‘t have to be an advanced statistician. Our comprehensive selection of machine learning algorithms can help you quickly get value from your big data. They include:
- Neural networks.
- Decision trees.
- Random forests.
- Associations and sequence discovery.
- Gradient boosting and bagging.
- Support vector machines.
- Nearest-neighbor mapping.
- k-means clustering.
- Self-organizing maps.
- Local search optimization techniques such as genetic algorithms.
- Expectation maximization.
- Multivariate adaptive regression splines.
- Bayesian networks.
- Kernel density estimation.
- Principal components analysis.
- Singular value decomposition.
- Gaussian mixture models.
- Sequential covering rule building.
有空可以自己研究研究。
SAS提供的机器学习算法
原文：http://www.cnblogs.com/postmodernist/p/4306850.html
