Title: Hierarchical loss for classification,
Abstract: Failing to distinguish between a sheepdog and a skyscraper should be worse
and penalized more than failing to distinguish between a sheepdog and a poodle;
after all, sheepdogs and poodles are both breeds of dogs. However, existing
metrics of failure (so-called "loss" or "win") used in textual or visual
classification/recognition via neural networks seldom view a sheepdog as more
similar to a poodle than to a skyscraper. We define a metric that, inter alia,
can penalize failure to distinguish between a sheepdog and a skyscraper more
than failure to distinguish between a sheepdog and a poodle. Unlike previously
employed possibilities, this metric is based on an ultrametric tree associated
with any given tree organization into a semantically meaningful hierarchy of a
classifier's classes.