Semantic Information Theory with Channles Matching (CM) Algorithm for Machine Learning: Chenguang Lu's Recent  Papers

鲁晨光最近文章——语义信息论和信道匹配算法用于机器学习

时间 Time 中文 PPT 说明 English PPT Note
2023-5 回顾学习函数和语义信息测度的进化 --进而理解深度学习 (评论文章) 　 中文网页

Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning

 　 　
2023-1 因果确证测度：从辛普森悖论到COVID-19 　 中文网页

Causal Confirmation Measures: From Simpson’s Paradox to COVID-19

 　 　
2021-8 用语义信息G测度解释和推广信息率-失真函数和最大熵分布       中文网页 Using the Semantic Information G Measure to Explain and Extend Rate-Distortion Functions and Maximum Entropy Distributions

 

   Published in Entropy 
2020-9 P-T概率框架用于语义通信、证伪、确证和贝叶斯推理      中文网页 The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning

 

   Published in Philosophies 
2020-5 用色觉译码模型解释互补处理，色觉进化，色盲等     中文网页 Explaining Color Evolution, Color Blindness, and Color Recognition by the Decoding Model of Color Vision

 PPT转PDF

   IIP2020杭州会议 
2020-1 信道确证和预测确证——从医学检验到乌鸦悖论 同下 中文翻译  Channels’ Confirmation and Predictions’ Confirmation:from the Medical Test to the Raven Paradox

　

    Published in Entropy 2020-4
2019-11 兼容Popper证伪思想的语义信息测度和确证度 PPT转PDF 复旦哲学讲座      
2019-8 语义信息G理论和逻辑贝叶斯推理for机器学习 　 英文全文

Semantic Information G Theory and Logical Bayesian Inference for Machine Learning

   Information——An Open Access Journal
2019 最大互信息分类——基于语义信息论的迭代算法 　 英文摘要和PPT An Iteration Algorithm for Maximum Mutual Information Classifications PPT IS4IS 2019 Conference
2019 语义信息理论及证伪和确证公式 　 中文全文和英文PPT Semantic Information G Theory with Formulas for Falsification and  Confirmation PPT IS4IS 2019 Conference
2018 EM算法的问题和出路  CM4Mix 包括更严格收敛证明 示范文件 From EM algorithm to CM-EM algorithm for global convergence of mixture models CM4Mix With strict convergence proof
Demo files
uploaded to Arxiv.org
2008 从贝叶斯推理到逻辑贝叶斯推理——一个新的数学框架用于语义通信和机器学习 　 阶段总结，右边是英文缩写 From Bayesian Inference to Logical Bayesian Inference: A New Mathematical Frame for Semantic Communication and Machine Learning New-Frame ICIS2018
2008 求标签外延和最大语义信息分类
多标签学习和分类浅谈——从语义通信角度看		 CM4Ml 中英文不同，包括补充解释 Semantic Channel and Shannon’s Channel Mutually Match for Multi-label Classification CM4Mlabel ICIS2018
2018 第三种贝叶斯定理用于语义通信和机器学习 右边是英文缩写 The Third Kind of Bayes’ Theorem Links Membership Functions to Likelihood Functions and Sampling Distributions ICCSIP2018
2018 语义信道和Shannon信道相互匹配求检验和估计的最大互信息和最大似然度 　  示范文件 Semantic channel and Shannon channel mutually match and iterate for tests and estimations with Maximum mutual information and maximum likelihood CM4MMI-ppt Demo files
IEEE Int. Conf. on Big Data & Smart Comp.- 2018 
2017 信道匹配算法用于混合模型（见英文） 　 　 Channels' Matching Algorithm for Mixture Models Cm4mix.ppt ICIS2017
2017 兼容Shannon，Popper， Fisher, and Zadeh思想的语义信息方法(见英文) Compatible 　 The semantic information methods compatible with Shannon, Popper, Fisher, and Zadeh's thoughts   ICFIE 2018, AISC 872 proceedings.
2017 EM算法是炼金术吗？ 　 博客文章 　 　 　

更早的关于语义信息论文章

CM算法示范文件包 The pakage of Demo files for Maximum mutual infrmation classifications and Mixture models

鲁晨光主页 

Papers on ArXiv

English Homapage

Ealier papers about information, information value, and philosophy