We are excited to release Learning with Limited Labeled Data, the latest report and prototype from Cloudera Fast Forward Labs.
Being able to learn with limited labeled data relaxes the stringent labeled data requirement for supervised machine learning. Our report focuses on active learning, a technique that relies on collaboration between machines and humans to label smartly.
Active learning makes it possible to build applications using a small set of labeled data, and enables enterprises to leverage their large pools of unlabeled data. In this blog post, we explore how active learning works. (For a higher level introduction, please see our previous blogpost.
The research itself is behind a paywall but you can see their write-up to get an idea of the topic.