Knowledge Management System of Institute of Theoretical Physics, CAS
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 | |
ISSN | 0253-6102 |
卷号 | 71期号:2页码:243-252 |
学科领域 | Physics |
学科门类 | Physics, Multidisciplinary |
DOI | 10.1088/0253-6102/71/2/243 |
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. |
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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 |
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