Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition (Adaptation, Learning, and Optimization)
Language: English
Pages: 321
ISBN: 3642378455
Format: PDF / Kindle (mobi) / ePub
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.
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Data Mining in Agriculture (Springer Optimization and Its Applications)
The function, S. Kiranyaz et al., Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Adaptation, Learning, and Optimization 15, DOI: 10.1007/978-3-642-37846-1_1, Ó Springer-Verlag Berlin Heidelberg 2014 1 2 1 Introduction which corresponds to the optimum solution of the problem. In mathematical terms, let f : S ! R be the objective function from a set S to the real numbers. An optimization technique searches for the extremum point x0 in S such that.
The major concepts presented in the book. This will allow practitioners and professionals to comprehend and use the presented techniques and adapt them to their own applications immediately. Contents 1 Introduction . . . . . . . . . . . 1.1 Optimization Era . . . 1.2 Key Issues . . . . . . . . 1.3 Synopsis of the Book References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Extension, MD PSO (to be discussed in Chap. 4). Finally, PSO_MDmain.cpp is the main console application, which offers three different entry point functions, main(), two of which are enabled with a compiler flag: BATCH_RUN or MOVING_PEAKS_BENCHMARK. The latter enables a special MD PSO application over a benchmark dynamic environment, which will be presented as the application for Sect. 5.2 in Chap. 5. The compiler flag BATCH_RUN can be enabled so as to test both PSO and/or MD PSO over all the.
Technical report, Department of Computer Science, University of Aarhus, 2002 2. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, H.-J. Zhang, Image classification with Kernelized spatial-context. IEEE Trans. Multimedia 12(4), 278–287 (2010). doi:10.1109/ TMM.2010.2046270 3. F. Van den Bergh, A.P. Engelbrecht, A new locally convergent particle swarm optimizer, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, (2002), pp. 96–101 148 5 Improving Global Convergence 4. A.
MD PSO with FGBF. Over-clustered samples are indicated with * (over-clustering). This is due to the use of a simple but quite impure validity index in (6.1) as the fitness function and for some complex clustering schemes it may, therefore, yield its minimum score at a slightly higher number of clusters. A sample clustering operation validating this fact is shown in Fig. 6.3. Note that the true number of clusters is 10, which is eventually reached at the beginning of the operation, yet the.