Machine learning applications need less data than has been assumed

A combined team of researchers from the University of British Columbia and the University of Alberta has found that at least some machine learning applications can learn from far fewer examples than has been assumed. In their paper published in the journal Nature Machine Intelligence, the group describes testing they carried out with machine learning applications created to predict certain types of molecular structures. Machine learning can be used in a wide variety of applications--one of the most well-known is learning to spot people or objects in photographs. Such applications typically require huge amounts of data for training. In this new effort, the researchers have found that in some instances, machine learning applications do not need such huge amounts of data to be useful.