Author

# Guoyin Wang

Other affiliations: Chongqing University, Intel, Xidian University ...read more

Bio: Guoyin Wang is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Rough set & Computer science. The author has an hindex of 38, co-authored 370 publications receiving 6161 citations. Previous affiliations of Guoyin Wang include Chongqing University & Intel.

##### Papers published on a yearly basis

##### Papers

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TL;DR: Here, the authors correct the errors made in Table 3 and equation (5) in the paper titled “Experimental Analyses of the Major Parameters Affecting the Intensity of Outbursts of Coal and Gas.”

Abstract: Here, we correct the errors made in Table 3 and equation (5) in the paper titled “Experimental Analyses of the Major Parameters Affecting the Intensity of Outbursts of Coal and Gas.” The purpose of this paper is to correct both the data input and the mathematical errors in Table 3 and equation (5).
The data input (Gas pressure (Mpa)) in Column 5, Table 3, should be as in the table mentioned in this paper.
The coefficients in equation (5) should be changed to
RI=−1.5874x1+2.866x2−0.3791x3+19.567x4,x4≥0.72.
(5)

512 citations

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TL;DR: Experimental results demonstrate that the proposed p-CNN is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.

213 citations

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TL;DR: A 2 nd -order generic normal cloud model, which establishes a relationship between normal cloud and normal distribution, is proposed, and an ideal backward cloud transformation algorithm is designed based on the mutually inverse features of FCT and BCT.

206 citations

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TL;DR: An efficient automatic method by extending the decision-theoretic rough set model to clustering, which is proved to stop automatically at the perfect number of clusters without manual interference, and a novel fast algorithm, FACA-DTRS, is devised based on the conclusion obtained in the validation of the ACA-D TRS algorithm.

192 citations

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TL;DR: The experimental results show that the proposed tree-based incremental overlapping clustering method can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show thatThe performance of proposed method is better than the compared algorithms in most of cases.

Abstract: Existing clustering approaches are usually restricted to crisp clustering, where objects just belong to one cluster; meanwhile there are some applications where objects could belong to more than one cluster. In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed; however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. In this paper, we propose a new tree-based incremental overlapping clustering method using the three-way decision theory. The tree is constructed from representative points introduced by this paper, which can enhance the relevance of the search result. The overlapping cluster is represented by the three-way decision with interval sets, and the three-way decision strategies are designed to updating the clustering when the data increases. Furthermore, the proposed method can determine the number of clusters during the processing. The experimental results show that it can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show that the performance of proposed method is better than the compared algorithms in most of cases.

188 citations

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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Microsoft

^{1}TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

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TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.

2,004 citations