Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba geometry and invariance in kernel based methods, Christopher J.C. Burges on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman making large-scale support vector machine learning practical, Thorsten Joachims fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin using support vector machines for time series prediction, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al support vector density estimation, Jason Weston et al combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.

Advances in kernel methods: support vector learning

This paper presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b/sup 2/=1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: 1) it is ellipse-specific so that even bad data will always return an ellipse; 2) it can be solved naturally by a generalized eigensystem, and 3) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other nonellipse-specific approaches would fail or require computationally expensive iterative refinements.

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Direct least squares fitting of ellipses

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and e1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

Recent approaches to global optimization problems through Particle Swarm Optimization

In this paper, a flexible yet efficient algorithm for solving engineering design optimization problems is presented. The algorithm is developed based on both binary-coded and realcoded genetic algorithms (GAs). Since both GAs are used, the variables involving discrete, continuous, and zero-one variables are handled quite efficiently. The algorithm restricts its search only to the permissible values of the variables, thereby reducing the search effort in converging to the optimum solution. The efficiency and ease of application of the proposed method is demonstrated by solving three different mechanical component design problems borrowed from the optimization literature. The proposed technique is compared with binarycoded genetic algorithms, Augmented Lagrange multiplier method, Branch and Bound method and Hooke and Jeeves pattern search method. In all cases, the solutions obtained using the proposed technique are superior than those obtained with other methods. These results are encouraging and suggest the use of the proposed technique to other engineering design problems.

A combined genetic adaptive search (GeneAS) for engineering design

Design optimization is a standard concept in engineering design, and in other disciplines which utilize mathematical decision-making methods. This textbook focuses on the close relationship between a design problem's mathematical model and the solution-driven methods which optimize it. Along with extensive material on modeling problems, this book also features useful techniques for checking whether a model is suitable for computational treatment. Throughout, key concepts are discussed in the context of why and when a particular algorithm may be successful, and a large number of examples demonstrate the theory or method right after it is presented. This book also contains step-by-step instructions for executing a design optimization project - from building the problem statement to interpreting the computer results. All chapters contain exercises from which instructors can easily build quizzes, and a chapter on 'principles and practice' offers the reader tips and guidance based on the authors' vast research and instruction experience.

Principles of Optimal Design: Modeling and Computation

Introduction. Chapter 1: Introduction to mental ray. What Is mental ray? Why Use mental ray? The Structure of mental ray. mental ray Integration. Command-Line Rendering and the Stand-Alone Renderer. mental ray Shaders and Shader Libraries. Indirect Illumination. Chapter 2: Rendering Algorithms. Introduction to Synthetic Lighting. Rendering under the Hood. mental ray Rendering Algorithms. Scanline Rendering in Depth. Raytrace Rendering in Depth. Hardware Rendering. Chapter 3: mental ray Output. mental ray Data Types. The Frame Buffer. Frame Buffer Options. mental ray Cameras. Output Statements. Chapter 4: Camera Fundamentals. Camera Basics and Aspect Ratios. Camera Lenses. Host Application Settings. Chapter 5: Quality Control. Sampling and Filtering in Host Applications. Raytrace Acceleration. Diagnostic and BSP Fine-Tuning. Chapter 6: Lights and Soft Shadows. mental ray Lights. Area Lights. Host Application Settings. Light Profiles. Chapter 7: Shadow Algorithms. Shadow Algorithms. Raytrace Shadows. Depth-Based Shadows. Stand-Alone and Host Settings. Chapter 8: Motion Blur. mental ray Motion Blur. Motion-Blur Options. Motion-Blur Render Algorithms. Host Settings. Chapter 9: The Fundamentals of Light and Shading Models. The Fundamentals of Light. Light Transport and Shading Models. mental ray Shaders. Chapter 10: mental ray Shaders and Shader Trees. Installing Custom Shaders. DGS and Dielectric Shading Models. Glossy Reflection and Refraction Shaders. Brushed Metals with the Glossy and Anisotropic Shaders. The Architectural (mia) Material. Chapter 11: mental ray Textures and Projections. Texture Space and Projections. mental ray Bump Mapping. mental ray Projection and Remapping Shaders. Host Application Settings. Memory Mapping, Pyramid Images, and Image Filtering. Chapter 12: Indirect Illumination. mental ray Indirect Illumination. Photon Shaders and Photon-Casting Lights. Indirect Illumination Options and Fine-Tuning. Participating Media (PM) Effects. Chapter 13: Final Gather and Ambient Occlusion. Final Gather Fundamentals. Final Gather Options and Techniques. Advanced Final Gather Techniques. Ambient Occlusion. Chapter 14: Subsurface Scattering. Advanced Shading Models. Nonphysical Subsurface Scattering. An Advanced Shader Tree. Physical Subsurface Scattering. Appendix: About the Companion CD. Index.

Optimization Theory and Applications

This is indeed a welcome addition to the set of several exciting introductory books on electromechanical energy conversion devices. It is a well-suited text for an introductory course for electrical engineering students with interest in power systems. The traditional subjects of electric machinery, i.e., transformers, dc machines, induction machines, and synchronous machines, are thoroughly covered without resorting to many complicated electromagnetic field theorems and equations. Many numerical examples demonstrate the theory and principles set forth in the text; the abundance of excellent figures, diagrams, and actual pictures serves to illustrate the important concepts. For the benefit of the students interested in control theory, simple dynamic analyses of several machines, using the sdomain techniques, are presented at the end of selected chapters. The book consists of seven chapters and four appendices. Chapters 1-4 cover transformers, dc machines, three phase system theory, and induction motors, respectively. The chapter on dc machines also contains analog simulation and CSMP programs for descniing the dynamics of a dc machine under different excitations. The remaining chapters are  dedicated to synchronous machines. Elementary  synchronous machines and measurement  of three-phase synchronous devices are topics of Chapters 5 and 7, respectively. Systems considerations of synchronous machines are covered in Chapter 6. Topics such as connection to an infinite bus, excitation systems, parallel operations, dynamic analysis, and swing equations are presented in this chapter. Consistent with the statement at the preface, the author presents material useful to students in control systems. Since stepping motors are used in a variety of control systems application, a section covering the theory, controls, and applications of these motors would have been a valuable addition to the book. The general organization of the book is reasonable in terms of both overall coverage and chapter headings. Each chapter includes a summary, a set of homework problems and a reference list. The text can be used in a two-semester course for electrical engineering students with a major in power systems. However, proper selection of the material in the text allows one to design a onesemester course in electric machine theory.

R. C. Desai

Papers

Optimization Theory and Applications

Introduction to Microprocessors