论文标题
迈向弱信息理论：使用支持向量机的弱点典型性解码可能会改善错误指数
Towards Weak Information Theory: Weak-Joint Typicality Decoding Using Support Vector Machines May Lead to Improved Error Exponents
论文作者
论文摘要
在本文中，作者报告了一种使用统计学习概念的方式，以通过离散的无内存通道进行通信，从而在错误指数方面获得优势。该研究利用科学计算套件MATLAB的模拟能力表明，所提出的解码方法的性能要比传统的联合典型性解码方法更好。通过修改构成解码错误的传统规范来确保优势。这是由“利用”嘈杂反馈的程序中使用的范式证明的，如果某些进一步的处理可以从中提取有用的信息，则不应将条件声明为错误。
In this paper, the authors report a way to use concepts from statistical learning to gain an advantage in terms of error exponents while communicating over a discrete memoryless channel. The study utilizes the simulation capability of the scientific computing package MATLAB to show that the proposed decoding method performs better than the traditional method of joint typicality decoding. The advantage is secured by modifying the traditional specification of what constitutes a decoding error. This is justified by the paradigm, also used in the program of `utilizing' noisy feedback, that one ought not to declare a condition as an error if some further processing can extract useful information from it.
