Neural nets : applications in geography
著者
書誌事項
Neural nets : applications in geography
(The GeoJournal library, v. 29)
Kluwer Academic Publishers, c1994
大学図書館所蔵 全12件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
Neural nets offer a new strategy for spatial analysis, and their application holds enormous potential for the geographic sciences. However, the number of studies that have utilized these techniques is limited. This lack of interest can be attributed, in part, to lack of exposure, to the use of extensive and often confusing jargon, and to the misapprehension that, without an underlying statistical model, the explanatory power of the neural net is very low. This text attacks all three issues, demonstrating a wide variety of neural net applications in geography in a simple manner, with minimal jargon. The volume presents an introduction to neural nets that describes some of the basic concepts, as well as providing a more mathematical treatise for those wanting further details on neural net architecture. The bulk of the text, however, is devoted to descriptions of neural net applications in such broad-ranging fields as census analysis, predicting the spread of AIDS, describing synoptic controls on mountain snowfall, examining the relationships between atmospheric circulation and tropical rainfall, and the remote sensing of polar cloud and sea ice characteristics.
The text illustrates neural nets employed in modes analogous to multiple regression analysis, cluster analysis, and maximum likelihood classification. Not only are the neural nets shown to be equal or superior to these more conventional methods, particularly where the relationships have a strong nonlinear component, but they are also shown to contain significant explanatory power. Several chapters demonstrate that the nets themselves can be decomposed to illuminate causative linkages between different events in both the physical and human environments.
目次
- Looks and Uses
- B.C. Hewitson, R.G. Crane. Neural Networks and Their Applications
- E.E. Clothiaux, C.M. Bachmann. Neuroclassification of Spatial Data
- S. Openshaw. Self-Organizing Maps - Application to Census Data
- K. Winter, B.C. Hewitson. Predicting Snowfall from Synoptic Circulation - a Comparison of Linear Regression and Neural Network Methodologies
- D.L. McGinnis. Neural Computing and the AIDS Pandemic - the Case of Ohio
- P.G. Gould. Precipitation Controls in Southern Mexico
- B.C. Hewitson, R.G. Crane. Classification of Arctic Cloud and Sea Ice Features in Multi-Spectral Satellite Data
- J.R. Key. Appendices: Neural Network Resources
- Fortran 77 Listing for Kohonen Self-Organizing Map.
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