Boosting over non-deterministic ZDDs

Takahiro Fujita, Kohei Hatano, Eiji Takimoto

研究成果: Contribution to journalArticle査読

抄録

We propose a new approach to large-scale machine learning, learning over compressed data: First compress the training data somehow and then employ various machine learning algorithms on the compressed data, with the hope that the computation time is significantly reduced when the training data is well compressed. As a first step toward this approach, we consider a variant of the Zero-Suppressed Binary Decision Diagram (ZDD) as the data structure for representing the training data, which is a generalization of the ZDD by incorporating non-determinism. For the learning algorithm to be employed, we consider a boosting algorithm called AdaBoost⁎ and its precursor AdaBoost. In this paper, we give efficient implementations of the boosting algorithms whose running times (per iteration) are linear in the size of the given ZDD.

本文言語英語
ページ(範囲)81-89
ページ数9
ジャーナルTheoretical Computer Science
806
DOI
出版ステータス出版済み - 2 2 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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