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Best investment strategy selection using asymptotic meta learning

Abstract:

Meta learning is an advanced field of machine learning where automatic learning algorithms are applied to acquire meta-knowledge for a set of learning algorithms called base learners. One of meta-learning purposes is to select the best base learners for certain kind of data set to support future learning process. Comparing average out-of-sample predictability with data bootstrapping is one of popular meta-learning algorithms to measure the performance of each base learner for time series data...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/sii.2017.8279191

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
Publisher:
IEEE
Pages:
72-76
Publication date:
2018-02-05
Acceptance date:
2017-12-11
Event title:
2017 IEEE/SICE International Symposium on System Integration (SII 2017)
Event location:
Taipei, Taiwan
Event website:
https://www.ieee-ras.org/component/rseventspro/event/1199-sii-2017-ieee-international-symposium-on-system-integration
Event start date:
2017-12-11
Event end date:
2017-12-14
DOI:
EISBN:
9781538622636
ISBN:
9781538622643
Language:
English
Keywords:
Pubs id:
1095470
Local pid:
pubs:1095470
Deposit date:
2020-03-27

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