TY - GEN

T1 - Boosting over non-deterministic ZDDs

AU - Fujita, Takahiro

AU - Hatano, Kohei

AU - Takimoto, Eiji

N1 - Funding Information:
Acknowledgments. We thank anonymous reviewers for helpful comments. This work is supported in part by JSPS KAKENHI Grant Number JP16J04621, JP16K00305 and JP15H02667, respectively.

PY - 2018

Y1 - 2018

N2 - 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 the first step, 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 boosting algorithm called AdaBoost* and its precursor AdaBoost. In this work, we give efficient implementations of the boosting algorithms whose running times (per iteration) are linear in the size of the given ZDD.

AB - 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 the first step, 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 boosting algorithm called AdaBoost* and its precursor AdaBoost. In this work, we give efficient implementations of the boosting algorithms whose running times (per iteration) are linear in the size of the given ZDD.

UR - http://www.scopus.com/inward/record.url?scp=85043326518&partnerID=8YFLogxK

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U2 - 10.1007/978-3-319-75172-6_17

DO - 10.1007/978-3-319-75172-6_17

M3 - Conference contribution

AN - SCOPUS:85043326518

SN - 9783319751719

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 195

EP - 206

BT - WALCOM

A2 - Rahman, M. Sohel

A2 - Sung, Wing-Kin

A2 - Uehara, Ryuhei

PB - Springer Verlag

T2 - 12th International Conference and Workshop on Algorithms and Computation, WALCOM 2018

Y2 - 3 March 2018 through 5 March 2018

ER -