Non-Standard Parameter Adaptation for Exploratory Data Analysis

ISBN: 3642040047

Category: Uncategorized

Tag: Database/SQL


Posted on 2018-10-07, by nokia241186.

Description




Non-Standard Parameter Adaptation for Exploratory Data Analysis By Wesam Ashour Barbakh
2009 | 240 Pages | ISBN: 3642040047 | PDF | 7 MB



Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets.We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods.We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.
DOWNLOAD
(Buy premium account for maximum speed and resuming ability)







Sponsored High Speed Downloads
9528 dl's @ 2950 KB/s
Download Now [Full Version]
8558 dl's @ 2573 KB/s
Download Link 1 - Fast Download
5932 dl's @ 2570 KB/s
Download Mirror - Direct Download



Search More...
Non-Standard Parameter Adaptation for Exploratory Data Analysis

Search free ebooks in ebookee.com!


Links
Download this book

No active download links here?
Please check the description for download links if any or do a search to find alternative books.


Related Books

  1. Ebooks list page : 37342
  2. 2017-12-11[PDF] Non-Standard Parameter Adaptation for Exploratory Data Analysis
  3. 2019-02-02SQL for Exploratory Data Analysis Essential Training XQZT
  4. 2018-08-14SQL for Exploratory Data Analysis Essential Training
  5. 2018-06-08SQL for Exploratory Data Analysis Essential Training
  6. 2018-01-28[PDF] Computational Intelligence for Big Data Analysis: Frontier Advances and Applications (Adaptation, Learning, and Optimization)
  7. 2016-05-08R Programming Exploratory Data Analysis For Professionals
  8. 2012-06-03PISA Data Analysis Manual: SPSS®
  9. 2012-06-03PISA Data Analysis Manual: SAS®
  10. 2019-01-31Methods for Statistical Data Analysis of Multivariate Observations, Second Edition
  11. 2019-01-31Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data Mining
  12. 2019-01-30Machine Learning for Big Data Analysis (Frontiers in Computational Intelligence)
  13. 2019-01-26PACKT- Hands On PySpark for Big Data Analysis XQZ T
  14. 2019-01-10Hands-On PySpark for Big Data Analysis
  15. 2019-01-06Hands-On PySpark for Big Data Analysis
  16. 2018-12-20Exploratory Data Analysis in Business and Economics: An Introduction Using SPSS, Stata, and Excel
  17. 2018-10-17Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python
  18. 2018-08-22Exploratory Data Analysis in Business and Economics An Introduction Using SPSS, Stata, and Excel
  19. 2018-08-08Excel Tricks Data Cleaning - Must for further Data Analysis (Update)
  20. 2018-08-08Excel Tricks Data Cleaning - Must for further Data Analysis (Updated)

Comments

No comments for "Non-Standard Parameter Adaptation for Exploratory Data Analysis".


    Add Your Comments
    1. Download links and password may be in the description section, read description carefully!
    2. Do a search to find mirrors if no download links or dead links.
    Back to Top