Chenguang Lu

Chenguang Lu
Changsha University & Liaoning Technical University

Bachelor of Engineering
Retired

About

49
Publications
7,385
Reads
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144
Citations
Introduction
My interests are semantic information theory, mechanism of color vision, beauty and evolution, statistical learning, probability theory (related to philosophy and mathematics). Currently, I am working on the P-T probability framework for the unification of statistics and logic.
Additional affiliations
August 1986 - July 2010
Changsha University
Position
  • Professor (Associate)
Description
  • I am retired now, some time as guest professor in Intelligence Engineering and Mathematics Institute, Liaoning Technical University, China
Education
September 1991 - September 1992
Beijing Normal University
Field of study
  • Fuzzy math and AI
February 1987 - February 1988
Niagara College Canada
Field of study
  • Computer Applications

Publications

Publications (49)
Research
Full-text available
The core idea of Darwin’s theory of sexual selection is beauty preference selection. But where did birds’ initial beauty preferences come from? The new answer is that birds’ appreciation for beauty come from needs for survival, such as good food, shelter, or water. 16 images reveal the mystery of birds' colourful plumages or apperances. (publishe...
Article
Full-text available
In the rate-distortion function and the Maximum Entropy (ME) method, Minimum Mutual Information (MMI) distributions and ME distributions are expressed by Bayes-like formulas, including Negative Exponential Functions (NEFs) and partition functions. Why do these non-probability functions exist in Bayes-like formulas? On the other hand, the rate-disto...
Preprint
Full-text available
Why can the Expectation-Maximization (EM) algorithm for mixture models converge? Why can different initial parameters cause various convergence difficulties? The Q-L synchronization theory explains that the observed data log-likelihood L and the complete data log-likelihood Q are positively correlated; we can achieve maximum L by maximizing Q. Acco...
Chapter
To apply information theory to more areas, the author proposed semantic information G theory, which is a natural generalization of Shannon’s information theory. This theory uses the P-T probability framework so that likelihood functions and truth functions (or membership functions), as well as sampling distributions, can be put into the semantic mu...
Chapter
This chapter aims to present a theoretical framework on the evolution stages of the machine brain and cognitive computation and systems for machine computation, learning and understanding. We divide AI subject into 2 branches—pure AI and applied AI (defined as an integration of AI with another subject: geoAI as an example). To stretch the continuat...
Chapter
Full-text available
This chapter aims to explain how to construct a machine learning system and summarize the current level of machine intelligence as cognitive computation. One-dimensional deep neural network is utilized for illustration and detection of recorded epileptic seizure activity in Electroencephalogram (EEG) segments is given as a practical application. Ma...
Chapter
To understand the basic evolution law of the machine brain, we need first understand machine cognition, which majorly depends on machine vision, machine touch and etc. Artificial intelligence (AI) has been rapidly developed in the latest decade and its importance to machine cognition has been widely recognized. But machine minds is still a dream ab...
Chapter
This chapter aims to explain the processes of machine cognition for a better understanding of environmental changes at the current level of machine intelligence and conjecture how evolution of the machine brain would change the future way of knowledge discovery (data mining) in environments sensing. In order to strengthen the continuity of Chap. 2,...
Chapter
This chapter finally presents a characterization of interdisciplinary evolution of the machine brain. Perspective schemes for rebuilding a real vision brain in the future are analyzed, along with the major principles to construct the machine brain, are presented, which include memory, thinking, imagination, feeling, speaking and other aspects assoc...
Chapter
This chapter aims to explain the pattern of machine understanding, utilizing medical test as a practical example. The explanation is based on the semantic information theory. After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortun...
Book
This book seeks to interpret connections between the machine brain, mind and vision in an alternative way and promote future research into the Interdisciplinary Evolution of Machine Brain (IEMB). It gathers novel research on IEMB, and offers readers a step-by-step introduction to the theory and algorithms involved, including data-driven approaches...
Preprint
Full-text available
Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P-T probability framework, which is assembled with...
Article
Full-text available
Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P–T probability framework, which is assembled with...
Preprint
Full-text available
The popular convergence theory of the EM algorithm explains that the observed incomplete data log-likelihood L and the complete data log-likelihood Q are positively correlated, and we can maximize L by maximizing Q. The Deterministic Annealing EM (DAEM) algorithm was hence proposed for avoiding locally maximal Q. This paper provides different concl...
Chapter
The author proposed the decoding model of color vision in 1987. International Commission on Illumination (CIE) recommended almost the same symmetric color model for color transform in 2006. For readers to understand the decoding model better, this paper first introduces the decoding model, then uses this model to explain the opponent-process, color...
Article
Full-text available
After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been prop...
Article
Full-text available
The Bayes classifier is often used because it is simple, and the Maximum Posterior Probability (MPP) criterion it uses is equivalent to the least error rate criterion. However, it has issues in the following circumstances: 1) If information instead of correctness is more important; we should use the maximum likelihood criterion or maximum informati...
Preprint
After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple discovered the Raven Paradox (RP). Then, Carnap used the logical probability increment as the confirmation measure. So far, many confirmation measures have been...
Article
Full-text available
An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this problem, semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Ma...
Experiment Findings
Full-text available
Example 1. U={1, 2, 3, …, 150}, true model ratio P*(y1)=0.5, true model parameters μ1*=65, μ2*=95, and σ1*= σ2*=10. Assume that the guessed ratios and parameters are P(y1)=P(y2)=0.5, μ1= μ1*, μ2=μ2*, and σ1= σ2=5.
Experiment Findings
Full-text available
Example 1. U={1, 2, 3, …, 150}, true model ratio P*(y1)=0.5, true model parameters μ1*=65, μ2*=95, and σ1*= σ2*=10. Assume that the guessed ratios and parameters are P(y1)=P(y2)=0.5, μ1= μ1*, μ2=μ2*, and σ1= σ2=5.
Chapter
For given age population prior distribution P(x) and the posterior distribution P(x|adult), how do we obtain the denotation of a label y = “adult”? With the denotation, e.g., the membership function of class {Adult}, we can make new probability prediction, e.g., likelihood function, for changed P(x). However, existing methods including Likelihood M...
Preprint
Full-text available
The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduces the Channels Matching (CM) algorithm for the MMI classifications of unseen instances. It also introduces some semantic information methods, which base the CM algorithm...
Experiment Findings
Full-text available
An example of maximum mutual information classification. After two iterations, MI reaches %99.99 of the convergent MMI.
Chapter
Popper and Fisher’s hypothesis testing thoughts are very important. However, Shannon’s information theory does not consider hypothesis testing. The combination of information theory and likelihood method is attracting more and more researchers’ attention, especially when they solve Maximum Mutual Information (MMI) and Maximum Likelihood (ML). This...
Preprint
Full-text available
The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. The popular convergence proof affirms that the likelihood increases with Q; Q is increasing in the M -step and non-decreasing in the E-step. The author found that (1) Q may and should decrease in some E-steps; (2) The Shannon channel from th...
Preprint
Full-text available
Bayesian Inference (BI) uses the Bayes' posterior whereas Logical Bayesian Inference (LBI) uses the truth function or membership function as the inference tool. LBI was proposed because BI was not compatible with the classical Bayes' prediction and didn't use logical probability and hence couldn't express semantic meaning. In LBI, statistical proba...
Preprint
Full-text available
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Shannon's channels and label selection is to let Shannon's channels match semantic channels. The Channel Matching (CM) algorithm is provided for multi-label classification....
Preprint
Full-text available
Why do many male birds display specific colorful patterns on their plumage? The demand-relationship theory explains that beauty preferences reflect human and birds' desire for approaching some objects; these patterns look beautiful because they resemble their ideal food sources or environments. Mutants that have enhanced human and birds' ability an...
Article
Full-text available
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. By the third kind of Bayes' theorem, we can directly convert a Shannon's channel into an optimized semantic channel. When a sample is not big enough, we can use a truth function with parameters to produce the likelihood f...
Conference Paper
Full-text available
To solve the Maximum Mutual Information (MMI) and Maximum Likelihood (ML) for tests, estimations, and mixture models, it is found that we can obtain a new iterative algorithm by the Semantic Mutual Information (SMI) and R(G) function proposed by Chenguang Lu (1993) (where R(G) function is an extension of information rate distortion function R(D), G...
Article
Full-text available
It is very difficult to solve the Maximum Mutual Information (MMI) or Maximum Likelihood (ML) for all possible Shannon Channels or uncertain rules of choosing hypotheses, so that we have to use iterative methods. According to the Semantic Mutual Information (SMI) and R(G) function proposed by Chenguang Lu (1993) (where R(G) is an extension of infor...
Data
Full-text available
Article
Full-text available
I proposed rate tolerance and discussed its relation to rate distortion in my book "A Generalized Information Theory" published in 1993. Recently, I examined the structure function and the complexity distortion based on Kolmogorov's complexity theory. It is my understanding now that complexity-distortion is only a special case of rate tolerance whi...
Article
Full-text available
A symmetrical model of color vision, the decoding model as a new version of zone model, was introduced. The model adopts new continuous-valued logic and works in a way very similar to the way a 3-8 decoder in a numerical circuit works. By the decoding model, Young and Helmholtz's tri-pigment theory and Hering's opponent theory are unified more natu...
Article
Full-text available
Based on the author’s new findings on the relationship between beauty and utility and the phenomenon of birds’ appreciating beauty, this paper tries to apply historical materialism to biological area in order to solve the problems with fragrance, sweetness, and beauty and to explain the cause of beauty sense. It also provides some pictures, which s...
Article
Full-text available
A generalized information formula related to logical probability and fuzzy set is deduced from the classical information formula. The new information measure accords with to Popper's criterion for knowledge evolution very much. In comparison with square error criterion, the information criterion does not only reflect error of a proposition, but als...
Article
Full-text available
Using fish-covering model, this paper intuitively explains how to extend Hartley's information formula to the generalized information formula step by step for measuring subjective information: metrical information (such as conveyed by thermometers), sensory information (such as conveyed by color vision), and semantic information (such as conveyed b...
Article
Full-text available
A generalized information theory is proposed as a natural extension of Shannon's information theory. It proposes that information comes from forecasts. The more precise and the more unexpected a forecast is, the more information it conveys. If subjective forecast always conforms with objective facts then the generalized information measure will be...
Article
Full-text available
A symmetrical model of color visions--the decoding model--has been established for us to understand color vision better. It adopts new continuous value logic or fuzzy logic and works in a way very similar to the way a 3-8 decoder in a numerical circuit does. Unlike a popular zone model of color vision, the decoding model has four pairs of opponent...

Questions

Questions (11)
Question
Darwin uses peahens' beauty preference to explain peacocks' colourful plumage. But Where did peahens' beauty preferences come from? I found that many birds mimic their favorite food items; the peacock mimics a berry tree. A paper introduces my discovery:
Welcome to discuss.

Projects

Projects (5)
Project
To clarify mistakes with the EM algorithm and the popular convergence theory for mixture models. To build a new convergence theory of mixture models.
Project
To clarify the origin of birds' beauty preferences and colorful plumage and human taste of beauty