Semantic Information Theory with Channles Matching (CM) Algorithm for Machine
Learning: Chenguang Lu's Recent Papers
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中文 |
PPT |
说明 |
English |
PPT |
Note |
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2024 |
语义变分贝叶斯——基于语义信息论的求解隐含变量
方法 |
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2024 |
语义信息 G 理论用于范围控制——兼顾合目的性和效率
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Semantic Information G
Theory for Range Control with Tradeoff between Purposiveness and
Efficiency
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| 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) |
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中文网页 |
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| 2023-1 |
因果确证测度：从辛普森悖论到COVID-19 |
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中文网页 |
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| 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 |
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中文网页 |
Using the Semantic Information G Measure to Explain and Extend Rate-Distortion Functions and Maximum Entropy Distributions
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Published in Entropy
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2020-9 |
P-T概率框架用于语义通信、证伪、确证和贝叶斯推理 |
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中文网页 |
The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning
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Published in Philosophies
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2020-5 |
用色觉译码模型解释互补处理，色觉进化，色盲等 |
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中文网页 |
Explaining Color Evolution, Color Blindness, and Color Recognition by the Decoding Model of Color Vision
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PPT转PDF
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IIP2020杭州会议
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| 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
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Published in Entropy 2020-4
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| 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 |
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| 2019-8 |
语义信息G理论和逻辑贝叶斯推理for机器学习 |
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英文全文 |
Semantic Information G Theory
and Logical Bayesian Inference
for
Machine Learning
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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 |
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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 |
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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 |
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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 |
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右边是英文缩写 |
The Third Kind of Bayes’ Theorem Links Membership Functions to
Likelihood Functions and Sampling Distributions |
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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 |
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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） |
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Channels' Matching Algorithm for Mixture Models
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Cm4mix.ppt |
ICIS2017 |
| 2017 |
兼容Shannon，Popper， Fisher, and
Zadeh思想的语义信息方法(见英文) |
Compatible |
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The
semantic information methods compatible with Shannon, Popper, Fisher,
and Zadeh's thoughts |
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ICFIE 2018, AISC 872 proceedings. |
| 2017 |
EM算法是炼金术吗？ |
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博客文章 |
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