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Machine learning in geosciences and remote sensing

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Geoscience Frontiers 2016年第1期7卷 3-10页

作者： David J.Lary Amir H.Alavi Amir H.Gandomi Annette L.Walker Hanson Center for Space science University of Texas at Dallas Department of Civil and Environmental Engineering Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University Aerosol and Radiation Section Naval Research Laboratory

Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

关键词： Machine learning Geosciences Remote sensing Regression Classification

Overlapping community detection combining content and link

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Journal of Zhejiang University-science C(computers and Electronics) 2012年第11期13卷 828-839页

作者： Zhou-zhou HE Zhong-fei(Mark)ZHANG Philip S.YU Zhejiang Provincial Key Laboratory of information Network Technology Department of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Department of computer science University of Illinois at ChicagoIL 60607USA

In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.

关键词： Overlapping Content Link Community detection

Progress of machine learning in geosciences:Preface

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Geoscience Frontiers 2016年第1期7卷 1-2页

作者： Amir H.Alavi Amir H.Gandomi David J.Lary Department of Civil and Environmental Engineering Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University Hanson Center for Space science University of Texas at Dallas

In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.

关键词： Progress of machine learning in geosciences BPNN

ERROR ESTIMATES FOR THE RECURSIVE LINEARIZATION OF INVERSE MEDIUM PROBLEMS

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Journal of Computational Mathematics 2010年第6期28卷 725-744页

作者： Gang Bao Faouzi Triki Department of Mathematics Michigan State University East Lansing MI 48824-1027 USA LMC-IMAG Universite Joseph Fourier I B.P. 53 38041 Grenoble Cedex 9 France

This paper is devoted to the mathematical analysis of a general recursive linearization algorithm for solving inverse medium problems with multi-frequency measurements. Under some reasonable assumptions, it is shown that the algorithm is convergent with error estimates. The work is motivated by our effort to analyze recent significant numerical results for solving inverse medium problems. Based on the uncertainty principle, the recursive linearization allows the nonlinear inverse problems to be reduced to a set of linear problems and be solved recursively in a proper order according to the measurements. As an application, the convergence of the recursive linearization algorithm [Chen, Inverse Problems 13（1997）, pp.253-282] is established for solving the acoustic inverse scattering problem.

关键词： Recursive linearization Tikhonov regularization Inverse problems Convergence analysis.