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Chapter 1: Bayesian Networks as a Decision Support Tool in Credit Scoring Domain
Table 1: Probability table P(<Customer Category>)
Table 2: Probability table P(<Customer Category >|<Debit>)
Table 3: Stages of credit granting process
Table 4: Areas of enterprise analysis
Table 5: The scoring table for the "profitability" area (normative point scale)
Table 6: The scoring tables for the rest of the objective factors areas
Table 7: Subjective factors, scoring tables
Table 8: The score corresponding to the class of the economic-financial position
Table 9: The score corresponding to the class of the economic-financial position and the previous credit performance
Table 10: Example-The score corresponding to factors
Table 11: The total score corresponding to qualitative and quantitative factors
Table 12: Conditional probability distributions in "quality of management" node
Table 13: The factors considered in Bayesian network structure of credit scoring system
Chapter 3: An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis
Table 1: Results of tests of difference in means for training and holdout samples
Table 2: Results of tests in training and holdout samples of heart disease data
Table 3: Results for tests in training and holdout samples for BUPA data set
Chapter 4: Guiding Knowledge Discovery Through Interactive Data Mining
Table 1: Textual presentation of clustering algorithm results
Table 2: Layered interaction model (Nielson, 1992)
Chapter 5: A Proposed Process for Performing Data Mining Projects
Table 1: Mapping observed activities to straw man
Chapter 6: Data Mining for Optimal Combination Demand Forecasts
Table 1: Summary statistics of the 26 items
Table 2: The alternative forecasting methods in each procedure
Table 3a: Forecasting results for sample A of 8 items
Table 3b: Forecasting results for sample B of 2 items
Table 4a: Results for common forecasting methods for sample A items
Table 4b: Results for common forecasting methods for sample B items
Table 5: F-statistics and comparisons of forecast error variances
Table 6: Results for common weights and forecasting method
Table 7: Summary of results
Table 8: A comparison of ARl and seasonal AR1 for the 10 items
Table 9: Summary of results if Holt-Winters method replaces Holt's method
Chapter 9: Utilization of Data Mining Techniques to Detect and Predict Accounting Fraud: A Comparison of Neural Networks and Discriminant Analysis
Table 1: Predicted group membership
Table 2: Percent good parts of neural net testing
Chapter 12: Combination Forecasts Based on Markov Chain Monte Carlo Estimation of the Mode
Table 1: Results for case 1 simulation study
Table 2: Results for case 2 simulation study
Table 3: Autocorrelation coefficients of arrival series
Table 4: Results of individual forecast method
Table 5: Results of combination forecast methods
Table 6: RMSE values of all the forecasting methods
Chapter 14: Connectionist and Evolutionary Models for Learning, Discovering and Forecasting Software Effort
Table 1: A few major software project failures in US and in UK
Table 2: Performance of three different structural designs during the training phase
Table 3: Performance of three different networks during the test phase
Table 4: Pair-wise comparison (t-test) results for difference in means for actual and predicted software effort
Table 5: Pair-wise comparison (t-test) results for difference in means for actual and predicted software effort
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Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174
Authors:
Parag Pendharkar
BUY ON AMAZON
Certified Ethical Hacker Exam Prep
Sniffers, Session Hijacking, and Denial of Service
Sniffers
Apply Your Knowledge
Hacking Web Servers
Limitation of Liability
FileMaker Pro 8: The Missing Manual
Bringing It All Together
Calculations and Data Types
Other Script Steps
Advanced Scripting
Security
Building Web Applications with UML (2nd Edition)
Security
The Process
The Vision
Summary
Controlled Controllers Pattern
Java for RPG Programmers, 2nd Edition
The World Of Java
The Java Onion
String Manipulation
Database Access
Appendix C Obtaining The Code Samples
C++ GUI Programming with Qt 3
Subclassing QMainWindow
Implementing Application Functionality
Graphics with QCanvas
Lists
Dynamic Language Switching
Excel Scientific and Engineering Cookbook (Cookbooks (OReilly))
Sorting Data
Manipulating Matrices
Building Support for Vectors
Fitting Nonlinear Curves Using Solver
Solving Linear Systems
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