| Description |
1 online resource (137 pages) |
| Contents |
Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; Conventions and Notation; CHAPTER 1: Introduction; 1.1 OPTIMIZATION IN STATISTICAL ANALYSIS; 1.2 STATISTICAL ANALYSIS: BRIEF OVERVIEW; 1.3 STATISTICAL REGRESSION; 1.3.1 Parametric regression; 1.3.2 Non-parametric regression; 1.4 HYPOTHESES TESTING; 1.5 NOTES; 1.5.1 Noise in the independent variable; 1.5.2 Statistical analysis and machine learning; CHAPTER 2: Stochastic Optimization Theory; 2.1 TERMINOLOGY; 2.2 CONVEX AND NON-CONVEX OPTIMIZATION PROBLEMS; 2.3 STOCHASTIC OPTIMIZATION |
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2.4 EXPLORATION AND EXPLOITATION2.5 BENCHMARKING; 2.6 TUNING; 2.7 BMR STRATEGY; 2.8 PSEUDO-RANDOM NUMBERS AND STOCHASTIC OPTIMIZATION; 2.9 NOTES; CHAPTER 3: Evolutionary Computation and Swarm Intelligence; 3.1 OVERVIEW; 3.2 EVOLUTIONARY COMPUTATION; 3.3 SWARM INTELLIGENCE; 3.4 NOTES; CHAPTER 4: Particle Swarm Optimization; 4.1 KINEMATICS: GLOBAL-BEST PSO; 4.2 DYNAMICS: GLOBAL-BEST PSO; 4.2.1 Initialization and termination; 4.2.2 Interpreting the velocity update rule; 4.2.3 Importance of limiting particle velocity; 4.2.4 Importance of proper randomization; 4.2.5 Role of inertia |
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4.2.6 Boundary condition4.3 KINEMATICS: LOCAL-BEST PSO; 4.4 DYNAMICS: LOCAL-BEST PSO; 4.5 STANDARDIZED COORDINATES; 4.6 RECOMMENDED SETTINGS FOR REGRESSION PROBLEMS; 4.7 NOTES; 4.7.1 Additional PSO variants; 4.7.2 Performance example; CHAPTER 5: PSO Applications; 5.1 GENERAL REMARKS; 5.1.1 Fitness function; 5.1.2 Data simulation; 5.1.3 Parametric degeneracy and noise; 5.1.4 PSO variant and parameter settings; 5.2 PARAMETRIC REGRESSION; 5.2.1 Tuning; 5.2.2 Results; 5.3 NON-PARAMETRIC REGRESSION; 5.3.1 Reparametrization in regression spline; 5.3.2 Results: Fixed number of breakpoints |
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5.3.3 Results: Variable number of breakpoints5.4 NOTES AND SUMMARY; 5.4.1 Summary; APPENDIX A: Probability Theory; A.1 RANDOM VARIABLE; A.2 PROBABILITY MEASURE; A.3 JOINT PROBABILITY; A.4 CONTINUOUS RANDOM VARIABLES; A.5 EXPECTATION; A.6 COMMON PROBABILITY DENSITY FUNCTIONS; APPENDIX B: Splines; B.1 DEFINITION; B.2 B-SPLINE BASIS; APPENDIX C: Analytical Minimization; C.1 QUADRATIC CHIRP; C.2 SPLINE-BASED SMOOTHING; Bibliography; Index |
| Summary |
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. Swarm Intelligence Methods for Statistical Regression 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. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Big data.
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Swarm intelligence.
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Computational intelligence.
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Regression analysis.
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Regression Analysis
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COMPUTERS -- Database Management -- Data Mining.
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COMPUTERS -- Machine Theory.
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data analysis.
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genetic algorithms.
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high-dimensional data.
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multi-agent systems.
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optimization.
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parametic regression.
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Big data
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Computational intelligence
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Regression analysis
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Swarm intelligence
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| Form |
Electronic book
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| ISBN |
9781351365031 |
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1351365037 |
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9781351365024 |
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1351365029 |
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9781351365017 |
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1351365010 |
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9781315151274 |
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1315151278 |
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