Computational, classification, financial, statistical and stochastic methods
著者
書誌事項
Computational, classification, financial, statistical and stochastic methods
(Innovation, entrepreneurship, management series, . Big data,
ISTE , Wiley, 2020
- : hardback
大学図書館所蔵 全1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 and index
内容説明・目次
内容説明
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
目次
Part 1. Computational Data Analysis and Methods 1. Semi-supervised Learning Based on Distributionally Robust Optimization, Jose Blanchet and Yang Kang. 2. Updating of PageRank in Evolving Treegraphs, Benard Abola, Pitos Seleka Biganda, Christopher Engstoerm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov. 3. Exploring The Relationship Between Ordinary PageRank, Lazy PageRank and Random Walk with Backstep PageRank for Different Graph Structures, Pitos Seleka Biganda, Benard Abola, Christopher Engstoerm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov. 4. On the Behavior of Alternative Splitting Criteria for CUB Model-based Trees, Carmela Cappelli, Rosaria Simone and Francesca Di Iorio. 5. Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models, Federica Nicolussi and Manuela Cazzaro. Part 2. Classification Data Analysis and Methods 6. Selection of Proximity Measures for a Topological Correspondence Analysis, Rafik Abdelssam. 7. Support Vector Machines: A Review and Applications in Statistical Process Monitoring, Anastasios Apsemidis and Stelios Psarakis. 8. Binary Classification Techniques: An Application on Simulated and Real Bio-medical Data, Fragkiskos G. Bersimis, Iraklis Varlamis, Malvina Vamvakari and Demosthenes B. Panagiotakos. 9. Some Properties of the Multivariate Generalized Hyperbolic Models, Stergios B. Fotopoulos, Venkata K. Jandhyala and Alex Paparas. 10. On Determining the Value of Online Customer Satisfaction Ratings A Case-based Appraisal, Jim Freeman. 11. Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix, Mariangela Sciandra, Antonio D Ambrosio and Antonella Plaia.
「Nielsen BookData」 より