Swarm Intelligence Methods for Statistical Regression

<p>A core task in statistical analysis especially in the era of Big Data is the fitting of flexible high-dimensional and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. <b><i>Swarm Intelligence Methods for Statistical Regression</i></b> describes methods from the field of computational swarm intelligence (SI) and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. </p><p>Features</p><p></p><ul> <br><br><p></p> <li>Provides a short self-contained overview of statistical data analysis and key results in stochastic optimization theory</li> <br><br> <br><br><p></p> <li>Focuses on methodology and results rather than formal proofs</li> <br><br> <br><br><p></p> <li>Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO)</li> <br><br> <br><br><p></p> <li>Uses concrete and realistic data analysis examples to guide the reader</li> <br><br> <br><br><p></p> <li>Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges</li> </ul><p></p>