Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 8, Pages 3668-3681Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2950779
Keywords
Machine learning; Optimization methods; Stochastic processes; Machine learning algorithms; Linear programming; Task analysis; Approximate Bayesian inference; deep neural network (DNN); machine learning; optimization method; reinforcement learning (RL)
Categories
Funding
- NSFC [61673179]
- Shanghai Sailing Program [17YF1404600]
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Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this article, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Finally, we explore and give some challenges and open problems for the optimization in machine learning.
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