Information geometry of U-Boost and Bregman divergence

Noboru Murata*, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi


研究成果: Article査読

126 被引用数 (Scopus)


We aim at an extension of AdaBoost to U-Boost, in the paradigm to build a stronger classification machine from a set of weak learning machines. A geometric understanding of the Bregman divergence defined by a generic convex function U leads to the U-Boost method in the framework of information geometry extended to the space of the finite measures over a label set. We propose two versions of U-Boost learning algorithms by taking account of whether the domain is restricted to the space of probability functions. In the sequential step, we observe that the two adjacent and the initial classifiers are associated with a right triangle in the scale via the Bregman divergence, called the Pythagorean relation. This leads to a mild convergence property of the U-Boost algorithm as seen in the expectation-maximization algorithm. Statistical discussions for consistency and robustness elucidate the properties of the U-Boost methods based on a stochastic assumption for training data.

ジャーナルNeural Computation
出版ステータスPublished - 2004 7月 1

ASJC Scopus subject areas

  • 人文科学(その他)
  • 認知神経科学


「Information geometry of U-Boost and Bregman divergence」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。