AbstractIn this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm. Linear-max algorithms learn with a special type of attribute called a sub-expert. A sub-expert is a vector attribute that has a value for each output class. The goal of the multi-class algorithm is to learn a linear function combining the sub-experts and to use this linear function to make correct class predictions. We will prove that, in the on-line mistake bounded model of learning, these multi-class learning algorithms have the same mistake bounds as a related two class linear-threshold algorithm. We will also show how sub-experts can be used to solve more traditional problems composed of real valued attributes. This leads to a natural extension of the algorithm to multi-class problems that contain both traditional attributes and sub-experts.
RightsThis Item is protected by copyright and/or related rights.You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use.For other uses you need to obtain permission from the rights-holder(s).