`

Timezone: »

A New Scheme for Boosting with an Average Margin Distribution Oracle
Ryotaro Mitsuboshi · Kohei Hatano · Eiji Takimoto

Boosting is a standard framework for learning a large-margin sparse ensemble of base hypotheses, where the algorithm assumes an oracle called the base learner, which returns a base hypothesis h with maximum edge Ei[yih(xi)] with respect to a given distribution over the sample ((x1, y1), . . . , (xm, ym)). In particular, for the l1-norm regularized soft margin optimization problem, there are several boosting algorithms that have theoretical iteration bound for finding ε- approximate solutions. They are not as fast as classical LPBoost by Demiriz et al., which has no non-trivial iteration bound. In this paper, we propose a new boosting scheme, where we assume a special base learner, which returns the average margin distribution vector Eh[(yih(xi))mi=1] with respect to a certain distribution over the base hypothesis class H. Under this scheme, we pro- pose a boosting algorithm whose iteration bound is O((r ln m)/ε2) and running time is O(m ln m) per iteration, where r is the VC dimension of H. Moreover, we also propose an efficient implementation for the new base learner, given that a relevant sub-class H|S of H, is represented by a non-deterministic version of the Zero-suppressed Decision Diagram (NZDD), where NZDD is a data structure for representing a family of sets succinctly.

#### More from the Same Authors

• 2021 : Poster Session 1 (gather.town) »
Hamed Jalali · Robert Hönig · Maximus Mutschler · Manuel Madeira · Abdurakhmon Sadiev · Egor Shulgin · Alasdair Paren · Pascal Esser · Simon Roburin · Julius Kunze · Agnieszka Słowik · Frederik Benzing · Futong Liu · Hongyi Li · Ryotaro Mitsuboshi · Grigory Malinovsky · Jayadev Naram · Zhize Li · Igor Sokolov · Sharan Vaswani