Bio-mimetic approaches in management science
著者
書誌事項
Bio-mimetic approaches in management science
(Advances in computational management science, v. 1)
Kluwer Academic Publishers, 1998
大学図書館所蔵 全6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
内容説明・目次
内容説明
Management Science is often confronted with optimization problems characterised by weak underlying theoretical models and complex constraints. Among them, one finds data analysis, pattern recognition (classification, multidimensional analysis, discriminant analysis) as well as modelling (forecasting, confirmatory analysis, expert system design). In recent years, biomimetic approaches have received growing attention from Marketing, Finance and Human Resource researchers and executives as effective tools for practically handling such problems. Biomimetic approaches include a variety of heuristic methods - such as neural networks, genetic algorithms, immunitary nets, cellular automata - that simulate nature's way of solving complex problems and, thus, can be considered as numerical transpositions of true life problem solving.
Bio-Mimetic Approaches in Management Science presents a selection of recent papers on biomimetic approaches and their application to Management Science. Most of these papers were presented at the last ACSEG/CAEMS International Congresses (Approches Connexionnistes en Sciences Economiques et de Gestion/Connectionnist Approaches in Economics and Management Science). All papers combine the discussion of conceptual issues with illustrative empirical applications, and contain detailed information on the way heuristics are practically implemented. The advantages and limits of the biomimetic approaches are discussed in several of the papers, either by comparing these approaches with more classical methods (logit models, clustering), or by investigating specific issues like overfitting and robustness. Synthesizing overviews are provided, as well as new tools for coping with some of the limitations of biomimetic methods.
目次
- Foreword. 1. The Potential of Neural Networks Evaluated within a Taxonomy of Marketing Applications
- S. Pandelidaki, A.N. Burgess. 2. Complexity Control and Generalization in Multilayer Perceptrons
- P. Gallinari, T. Cibas. 3. Application of Neural Networks to Bond Rating and House Pricing
- H. Daniels, et al. 4. Connexionist Approach and Corporate Distress Diagnosis: A Contribution to the Processing of Incomplete Information
- J.-F. Casta, B. Prat. 5. Comparison of the Predictivity of a Neural Network with Backpropagation with Those Using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring
- P. Desmet. 6. Comparison of Discriminant Analysis and Neural Networks Application for the Detection of Company Failures
- M. Bardos, Wenhua Zhu. 7. Using Artificial Neural Nets to Specify and Estimate Aggregate Reference Price Models
- M. Natter, H. Hruschka. 8. Towards Connectionist Merchandising: A Conceptual and Operational Definition
- Ph.R. Demontrond, D. Thiel. 9. A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities
- A.N. Burgess, S. Pandelidaki. 10. Optimization by a Genetic Algorithm of Stochastic Linear Models of Time Series
- R. Bone, et al. 11. A Bio-Mimetic Clusterwise Regression Algorithm for Consumer Segmentation
- J.-M. Aurifeille. 12. Hybrid Genetic Learning of Hidden Markov Models for Time Series Prediction
- M. Slimane, et al. 13. Learning How to Regulate a Polluter with Unknown Characteristics: An Application of Genetic Algorithms to a Game of Dynamic Pollution Control
- T. Vallee, C.Deissenberg.
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