Source: Google, Oct 2016
<Read source for details>
Expander’s technology draws inspiration from how humans learn to generalize and bridge the gap between what they already know (labeled information) and novel, unfamiliar observations (unlabeled information). Known as “semi-supervised” learning, this powerful technique enables us to build systems that can work in situations where training data may be sparse.
One of the key advantages to a graph-based semi-supervised machine learning approach is the fact that (a) one models labeled and unlabeled data jointly during learning, leveraging the underlying structure in the data, (b) one can easily combine multiple types of signals (for example, relational information fromKnowledge Graph along with raw features) into a single graph representation and learn over them.
This is in contrast to other machine learning approaches, such as neural network methods, in which it is typical to first train a system using labeled data with features and then apply the trained system to unlabeled data.