TY - JOUR

T1 - Large margin hierarchical classification

AU - Dekel, Ofer

AU - Keshet, Joseph

AU - Singer, Yoram

PY - 2004/12/1

Y1 - 2004/12/1

N2 - We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counter-part. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.

AB - We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counter-part. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.

UR - http://www.scopus.com/inward/record.url?scp=14344256758&partnerID=8YFLogxK

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

JO - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

JF - Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

ER -