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E-grāmata: Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

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  • Formāts: PDF+DRM
  • Sērija : Adaptation, Learning, and Optimization 15
  • Izdošanas datums: 16-Jul-2013
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642378461
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Multidimensional Particle Swarm Optimization for Machine Learning and Pattern RecognitionPalielināt
  • Formāts: PDF+DRM
  • Sērija : Adaptation, Learning, and Optimization 15
  • Izdošanas datums: 16-Jul-2013
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642378461
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This book explores multidimensional particle swarm optimization, a technique developed by the authors and presented in a well-defined algorithmic approach. All featured applications are supported with fully documented source code as well as sample datasets.

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

 

After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.

 

The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

Recenzijas

From the book reviews:

This book has enough material to be used as a reference text in research in areas of biomedical signal processing, classification, and clustering. Alternatively, it can be employed as an extra textbook in a graduate course on optimization. Its clear style and strong practical orientation make the book an excellent addition to the bookshelf of any researcher dealing with optimization problems in many dimensions. (Alexander Tzanov, Computing Reviews, July, 2014)

1 Introduction
1(12)
1.1 Optimization Era
2(2)
1.2 Key Issues
4(3)
1.3 Synopsis of the Book
7(6)
References
10(3)
2 Optimization Techniques: An Overview
13(32)
2.1 History of Optimization
13(16)
2.2 Deterministic and Analytic Methods
29(4)
2.2.1 Gradient Descent Method
29(1)
2.2.2 Newton-Raphson Method
30(2)
2.2.3 Nelder-Mead Search Method
32(1)
2.3 Stochastic Methods
33(4)
2.3.1 Simulated Annealing
33(2)
2.3.2 Stochastic Approximation
35(2)
2.4 Evolutionary Algorithms
37(8)
2.4.1 Genetic Algorithms
37(4)
2.4.2 Differential Evolution
41(2)
References
43(2)
3 Particle Swarm Optimization
45(38)
3.1 Introduction
45(1)
3.2 Basic PSO Algorithm
46(3)
3.3 Some PSO Variants
49(6)
3.3.1 Tribes
51(2)
3.3.2 Multiswarms
53(2)
3.4 Applications
55(19)
3.4.1 Nonlinear Function Minimization
55(2)
3.4.2 Data Clustering
57(4)
3.4.3 Artificial Neural Networks
61(13)
3.5 Programming Remarks and Software Packages
74(9)
References
80(3)
4 Multi-dimensional Particle Swarm Optimization
83(18)
4.1 The Need for Multi-dimensionality
83(2)
4.2 The Basic Idea
85(2)
4.3 The MD PSO Algorithm
87(5)
4.4 Programming Remarks and Software Packages
92(9)
4.4.1 MD PSO Operation in PSO_MDlib Application
92(2)
4.4.2 MD PSO Operation in PSOTestApp Application
94(5)
References
99(2)
5 Improving Global Convergence
101(50)
5.1 Fractional Global Best Formation
102(14)
5.1.1 The Motivation
102(1)
5.1.2 PSO with FGBF
102(2)
5.1.3 MD PSO with FGBF
104(1)
5.1.4 Nonlinear Function Minimization
104(12)
5.2 Optimization in Dynamic Environments
116(12)
5.2.1 Dynamic Environments: The Test Bed
116(1)
5.2.2 Multiswarm PSO
117(1)
5.2.3 FGBF for the Moving Peak Benchmark for MPB
118(1)
5.2.4 Optimization over Multidimensional MPB
119(1)
5.2.5 Performance Evaluation on Conventional MPB
120(4)
5.2.6 Performance Evaluation on Multidimensional MPB
124(4)
5.3 Who Will Guide the Guide?
128(13)
5.3.1 SPSA Overview
130(1)
5.3.2 SA-Driven PSO and MD PSO Applications
131(3)
5.3.3 Applications to Non-linear Function Minimization
134(7)
5.4 Summary and Conclusions
141(1)
5.5 Programming Remarks and Software Packages
142(9)
5.5.1 FGBF Operation in PSO_MDlib Application
143(1)
5.5.2 MD PSO with FGBF Application Over MPB
144(3)
References
147(4)
6 Dynamic Data Clustering
151(36)
6.1 Dynamic Data Clustering via MD PSO with FGBF
152(8)
6.1.1 The Theory
152(3)
6.1.2 Results on 2D Synthetic Datasets
155(5)
6.1.3 Summary and Conclusions
160(1)
6.2 Dominant Color Extraction
160(11)
6.2.1 Motivation
160(3)
6.2.2 Fuzzy Model over HSV-HSL Color Domains
163(1)
6.2.3 DC Extraction Results
164(6)
6.2.4 Summary and Conclusions
170(1)
6.3 Dynamic Data Clustering via SA-Driven MD PSO
171(5)
6.3.1 SA-Driven MD PSO-Based Dynamic Clustering in 2D Datasets
171(3)
6.3.2 Summary and Conclusions
174(2)
6.4 Programming Remarks and Software Packages
176(11)
6.4.1 FGBF Operation in 2D Clustering
176(3)
6.4.2 DC Extraction in PSOTestApp Application
179(4)
6.4.3 SA-DRIVEN Operation in PSOTestApp Application
183(2)
References
185(2)
7 Evolutionary Artificial Neural Networks
187(44)
7.1 Search for the Optimal Artificial Neural Networks: An Overview
188(2)
7.2 Evolutionary Neural Networks by MD PSO
190(15)
7.2.1 PSO for Artificial Neural Networks: The Early Attempts
190(1)
7.2.2 MD PSO-Based Evolutionary Neural Networks
191(2)
7.2.3 Classification Results on Synthetic Problems
193(7)
7.2.4 Classification Results on Medical Diagnosis Problems
200(3)
7.2.5 Parameter Sensitivity and Computational Complexity Analysis
203(2)
7.3 Evolutionary RBF Classifiers for Polarimetric SAR Images
205(12)
7.3.1 Polarimetric SAR Data Processing
207(2)
7.3.2 SAR Classification Framework
209(2)
7.3.3 Polarimetric SAR Classification Results
211(6)
7.4 Summary and Conclusions
217(1)
7.5 Programming Remarks and Software Packages
218(13)
References
227(4)
8 Personalized ECG Classification
231(28)
8.1 ECG Classification by Evolutionary Artificial Neural Networks
233(11)
8.1.1 Introduction and Motivation
233(2)
8.1.2 ECG Data Processing
235(4)
8.1.3 Experimental Results
239(5)
8.2 Classification of Holter Registers
244(9)
8.2.1 The Related Work
245(1)
8.2.2 Personalized Long-Term ECG Classification: A Systematic Approach
246(4)
8.2.3 Experimental Results
250(3)
8.3 Summary and Conclusions
253(2)
8.4 Programming Remarks and Software Packages
255(4)
References
257(2)
9 Image Classification and Retrieval by Collective Network of Binary Classifiers
259(36)
9.1 The Era of CBIR
260(2)
9.2 Content-Based Image Classification and Retrieval Framework
262(8)
9.2.1 Overview of the Framework
263(1)
9.2.2 Evolutionary Update in the Architecture Space
264(1)
9.2.3 The Classifier Framework: Collective Network of Binary Classifiers
265(5)
9.3 Results and Discussions
270(10)
9.3.1 Database Creation and Feature Extraction
271(1)
9.3.2 Classification Results
272(5)
9.3.3 CBIR Results
277(3)
9.4 Summary and Conclusions
280(1)
9.5 Programming Remarks and Software Packages
281(14)
References
293(2)
10 Evolutionary Feature Synthesis
295
10.1 Introduction
295(2)
10.2 Feature Synthesis and Selection: An Overview
297(2)
10.3 The Evolutionary Feature Synthesis Framework
299(7)
10.3.1 Motivation
299(2)
10.3.2 Evolutionary Feature Synthesis Framework
301(5)
10.4 Simulation Results and Discussions
306(8)
10.4.1 Performance Evaluations with Respect to Discrimination and Classification
307(2)
10.4.2 Comparative Performance Evaluations on Content-Based Image Retrieval
309(5)
10.5 Programming Remarks and Software Packages
314
References
321
Prof. Serkan Kiranyaz worked as a researcher in Nokia Research Center and later in Nokia Mobile Phones in Tampere, Finland. He received his Ph.D. in 2005 and qualified as a Docent in 2007 from the Inst. of Signal Processing of Tampere Univ. of Technology, where he is currently a professor. He is the architect and principal developer of the ongoing content-based multimedia indexing and retrieval framework, MUVIS. His interests include swarm intelligence, stochastic optimization techniques, evolutionary neural networks, content-based multimedia indexing, browsing and retrieval algorithms, audio analysis and audio-based multimedia retrieval, object extraction, and biomedical signal analysis.

 

Dr. Turker Ince received his Ph.D. from the Univ. of Massachusetts, Amherst, in 2001 in electrical engineering. He was a research assistant in the Microwave Remote Sensing Laboratory of UMass-Amherst from 1996 to 2001, and he worked as a design engineer at Aware, Inc., Boston from 2001 to 2004, and at Texas Instruments, Inc., Dallas from 2004 to 2006. He is currently an associate professor in the Dept. of Electrical and Electronics Engineering of Izmir University of Economics, Turkey. He teaches and conducts research in the areas of remote sensing, radar systems and signal processing, neural networks, and evolutionary optimization.

 

Prof. Moncef Gabbouj received his Ph.D. from Purdue University in 1989 in electrical engineering. He is an Academy Professor with the Academy of Finland (2011-2015), and a Professor in the Dept. of Signal Processing of Tampere University of Technology, Finland. He is a Fellow of the IEEE, he has chaired many research and education projects and technical committees, and he has edited related journal issues. His interests include multimedia content-based analysis, indexing and retrieval, swarm optimization, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. He has coauthoredover 500 publications.
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