Collaborative Learning of Human and Computer: Supervised Actor-Critic based Collaboration Scheme

被引：0

作者：

Devanga, Ashwin ^{[1
]}

Yamauchi, Koichiro ^{[2
]}

机构：

[1] Indian Inst Technol Guwahati, Gauhati, India

[2] Chubu Univ, Ctr Engn, Kasugai, Aichi, Japan

来源：

ICPRAM: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS | 2019年

关键词：

Actor-Critic Model; Kernel Machine; Learning on a Budget; Super Neural Network; Colbagging; Supervised Learning; Reinforcement Learning; Collaborative Learning Scheme between Human and Learning Machine;

D O I：

10.5220/0007568407940801

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent large-scale neural networks show a high performance to complex recognition tasks but to get such ability, it needs a huge number of learning samples and iterations to optimize it's internal parameters. However, under unknown environments, learning samples do not exist. In this paper, we aim to overcome this problem and help improve the learning capability of the system by sharing data between multiple systems. To accelerate the optimization speed, the novel system forms a collaboration with human and reinforcement learning neural network and for data sharing between systems to develop a super neural network.

引用

页码：794 / 801

页数：8

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