Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables

no code implementations • 31 May 2016 • Hui Shen, Dehua Li, Hong Wu, Zhaoxiang Zang

Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network.

Denoising Variable Selection

Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning

no code implementations • 26 Apr 2016 • Zhaoxiang Zang, Zhao Li, Junying Wang, Zhiping Dan

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones.

reinforcement-learning Reinforcement Learning (RL)