Accession Number:

AD1037815

Title:

Towards a Better Distributed Framework for Learning Big Data

Descriptive Note:

Technical Report,14 May 2015,13 May 2017

Corporate Author:

National Taiwan University Taipei Taiwan

Personal Author(s):

Report Date:

2017-06-14

Pagination or Media Count:

27.0

Abstract:

This work aimed at solving issues in distributed machine learning. The PIs team proposed three directions to work on. First, they designed solutions to speed up the alternating direction method of multipliers ADMM for distributed data. Second, they focused on a client-server learning scenario in which an online, semi-supervised learning approach is designed to reduce the communication load. Finally, the team proposed the parallel least-squares policy iteration parallel LSPI to parallelize a reinforcement policy learning.

Subject Categories:

  • Electricity and Magnetism

Distribution Statement:

APPROVED FOR PUBLIC RELEASE