Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

ISBN-13: 9781484233351 / Angielski / Miękka / 2017 / 517 str.

Timothy Masters

Spis treści

1. Assessment of Numeric Predictions

2. Assessment of Class Predictions

3. Resampling for Assessing Parameter Estimates

4. Resampling for Assessing Prediction and Classification

5. Miscellaneous Resampling Techniques

6. Combining Numeric Predictions

7. Combining Classification Models

8. Gaiting Methods

9. Information and Entropy

References

O autorze

Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored four books on practical applications of neural networks: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995).

Blurb

Carry out practical, real-life assessments of the performance of prediction and classification models written in C++. This book discusses techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment. 

Finally, the last part of the book is devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique. 

You will:

• Discover the hidden pitfalls that lurk in the model development process
  
• Work with some of the most powerful model enhancement algorithms that have emerged recently
  
• Effectively use and incorporate the C++ code in your own data analysis projects
  
• Combine classification models to enhance your projects