ITP OpenIR  > SCI期刊论文
Zhou, HJ
Active Online Learning in the Binary Perceptron Problem
发表期刊COMMUNICATIONS IN THEORETICAL PHYSICS
语种英语
关键词STATISTICAL-MECHANICS EXAMPLES INFERENCE ALGORITHM NETWORKS STORAGE
摘要The binary perceptron is the simplest artificial neural network formed by N input units and one output unit, with the neural states and the synaptic weights all restricted to +/- 1 values. The task in the teacher-student scenario is to infer the hidden weight vector by training on a set of labeled patterns. Previous efforts on the passive learning mode have shown that learning from independent random patterns is quite inefficient. Here we consider the active online learning mode in which the student designs every new Ising training pattern. We demonstrate that it is mathematically possible to achieve perfect (error-free) inference using only N designed training patterns, but this is computationally unfeasible for large systems. We then investigate two Bayesian statistical designing protocols, which require 2.3N and 1.9N training patterns, respectively, to achieve error-free inference. If the training patterns are instead designed through deductive reasoning, perfect inference is achieved using N+log(2)N samples. The performance gap between Bayesian and deductive designing strategies may be shortened in future work by taking into account the possibility of ergodicity breaking in the version space of the binary perceptron.
2019
ISSN0253-6102
卷号71期号:2页码:243-252
学科领域Physics
学科门类Physics, Multidisciplinary
DOI10.1088/0253-6102/71/2/243
收录类别SCIE
引用统计
文献类型期刊论文
条目标识符http://ir.itp.ac.cn/handle/311006/23497
专题SCI期刊论文
计算平台成果
作者单位1.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Zhou, HJ. Active Online Learning in the Binary Perceptron Problem[J]. COMMUNICATIONS IN THEORETICAL PHYSICS,2019,71(2):243-252.
APA Zhou, HJ.(2019).Active Online Learning in the Binary Perceptron Problem.COMMUNICATIONS IN THEORETICAL PHYSICS,71(2),243-252.
MLA Zhou, HJ."Active Online Learning in the Binary Perceptron Problem".COMMUNICATIONS IN THEORETICAL PHYSICS 71.2(2019):243-252.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Active Online Learni(773KB)期刊论文作者接受稿开放获取CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhou, HJ]的文章
百度学术
百度学术中相似的文章
[Zhou, HJ]的文章
必应学术
必应学术中相似的文章
[Zhou, HJ]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。