"Our algorithm learned to correctly classify difficult, borderline cases - distinguishing the texture of hair versus the texture of the background," said Aarabi. With this model, called heuristic training, humans provide direct instructions that are used to pre-classify training samples rather than a set of fixed examples.Trainers programme the algorithm with guidelines such as "sky is likely to be varying shades of blue," and "pixels near the top of the image are more likely to be sky than pixels at the bottom. "This heuristic training approach holds considerable promise for addressing one of the biggest challenges for neural networks: making correct classifications of previously unknown or unlabelled data.This is crucial for applying machine learning to new situations, such as correctly identifying cancerous tissues for medical diagnostics, or classifying all the objects surrounding and approaching a self-driving car. "Applying heuristic training to hair segmentation is just a start," said Guo. "We are keen to apply our method to other fields and a range of applications, from medicine to transportation," Guo added.The study was published in the journal IEEE Transactions on Neural Networks and Learning Systems.

Source: Economic Times November 17, 2016 11:46 UTC