Large number of features is a challenge. Another one is the emerging stream mining application.
Other than these two challenges, Mahout may be suitable for ensemble learning, where parallel computing will save time greatly. Shunkai -----邮件原件----- 发件人: Ted Dunning [mailto:[EMAIL PROTECTED] 发送时间: 2008年3月21日 9:02 收件人: [email protected] 主题: Re: application of GSoC I think a better description is that this project is about ML algorithms that need large scale. If you have very inexpensive feature selection that can run sequentially, then it probably doesn't matter to use hadoop/mahout for that. Some forms of feature extraction is very expensive, however, and could definitely benefit from parallelism. For instance, you could imagine that the feature extraction step involves a large scale non-deterministic clustering. It might even be that the the feature extraction requires parallel processing, but the actual learning algorithm does not. On 3/20/08 5:57 PM, "Hao Zheng" <[EMAIL PROTECTED]> wrote: > Another question, this project is all about the ML algorithm itself? > all we will deal with is feature vectors/matrix constructed already? > that is, the project will not include feature selection part of ML, > e.g. extracting feature vector from a document collection?
