Name
Abstract | ||
|---|---|---|
Loss functions are central to machine learning because they are the means by which the quality of a prediction is evaluated. Any loss that is not proper, or can not be transformed to be proper via a link function is inadmissible. All admissible losses for n-class problems can be obtained in terms of a convex body in \mathbbR^n. We show this explicitly and show how some existing results simplify when viewed from this perspective. This allows the development of a rich algebra of losses induced by binary operations on convex bodies (that return a convex body). Furthermore it allows us to define an “inverse loss” which provides a universal “substitution function” for the Aggregating Algorithm. In doing so we show a formal connection between proper losses and norms. |
Year | Venue | Field |
|---|---|---|
2014 | COLT | Inverse,Mathematical optimization,Convex body,Computer science,Link function,Regular polygon,Binary operation |
DocType | Citations | PageRank |
Conference | 2 | 0.43 |
References | Authors | |
7 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert C. Williamson | 1 | 4191 | 755.22 |