Evaluation of Machine Learning Methods on SPiCe

Ichinari Sato, Kaizaburo Chubachi,  Diptarama
; Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:149-153, 2017.

Abstract

In this paper, we introduce methods that we used to solve problems from the sequence prediction competition called SPiCe. We train a model from sequences in train data on each problem, and then predict a next symbol following each sequence in test data. We implement several methods to solve these problems. The experiment results show that XGBoost and neural network approaches have good performance overall.

Cite this Paper


BibTeX
@InProceedings{pmlr-v57-sato16, title = {Evaluation of Machine Learning Methods on {SPiCe}}, author = {Ichinari Sato and Kaizaburo Chubachi and Diptarama}, booktitle = {Proceedings of The 13th International Conference on Grammatical Inference}, pages = {149--153}, year = {2017}, editor = {Sicco Verwer and Menno van Zaanen and Rick Smetsers}, volume = {57}, series = {Proceedings of Machine Learning Research}, address = {Delft, The Netherlands}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v57/sato16.pdf}, url = {http://proceedings.mlr.press/v57/sato16.html}, abstract = {In this paper, we introduce methods that we used to solve problems from the sequence prediction competition called SPiCe. We train a model from sequences in train data on each problem, and then predict a next symbol following each sequence in test data. We implement several methods to solve these problems. The experiment results show that XGBoost and neural network approaches have good performance overall.} }
Endnote
%0 Conference Paper %T Evaluation of Machine Learning Methods on SPiCe %A Ichinari Sato %A Kaizaburo Chubachi %A Diptarama %B Proceedings of The 13th International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2017 %E Sicco Verwer %E Menno van Zaanen %E Rick Smetsers %F pmlr-v57-sato16 %I PMLR %J Proceedings of Machine Learning Research %P 149--153 %U http://proceedings.mlr.press %V 57 %W PMLR %X In this paper, we introduce methods that we used to solve problems from the sequence prediction competition called SPiCe. We train a model from sequences in train data on each problem, and then predict a next symbol following each sequence in test data. We implement several methods to solve these problems. The experiment results show that XGBoost and neural network approaches have good performance overall.
RIS
TY - CPAPER TI - Evaluation of Machine Learning Methods on SPiCe AU - Ichinari Sato AU - Kaizaburo Chubachi AU - Diptarama BT - Proceedings of The 13th International Conference on Grammatical Inference PY - 2017/01/16 DA - 2017/01/16 ED - Sicco Verwer ED - Menno van Zaanen ED - Rick Smetsers ID - pmlr-v57-sato16 PB - PMLR SP - 149 DP - PMLR EP - 153 L1 - http://proceedings.mlr.press/v57/sato16.pdf UR - http://proceedings.mlr.press/v57/sato16.html AB - In this paper, we introduce methods that we used to solve problems from the sequence prediction competition called SPiCe. We train a model from sequences in train data on each problem, and then predict a next symbol following each sequence in test data. We implement several methods to solve these problems. The experiment results show that XGBoost and neural network approaches have good performance overall. ER -
APA
Sato, I., Chubachi, K. & Diptarama, . (2017). Evaluation of Machine Learning Methods on SPiCe. Proceedings of The 13th International Conference on Grammatical Inference, in PMLR 57:149-153

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