Towards a Better Distributed Framework for Learning Big Data

Abstract

This work aimed at solving issues in distributed machine learning. The PI's 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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 14, 2017
Accession Number
AD1037815

Entities

People

  • Shou-De Lin

Organizations

  • National Taiwan University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Actuators
  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Bernoulli Distribution
  • Cellular Networks
  • Classification
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Data Science
  • Data Sets
  • Digital Data
  • Information Science
  • Machine Learning
  • Markov Chains
  • Mobile Devices
  • Optimization
  • Probability
  • Sampling
  • Semi-Supervised Learning
  • Standards
  • Supervised Machine Learning
  • Training
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks