The Parameter Space Investigation (PSI) method was developed to help engineers with a wide range of multicriteria optimization problems, such as design, identification, design of control systems, and operational development of prototypes. This unique resource shows you how to use PSI to construct a feasible solution set without limitations on the number of parameters and criteria. The book presents visualization tools that are used to construct the feasible solution set, conduct multicriteria analysis, and correct the initial problem statement. You explore topics that have not been covered in any other books, including multicriteria analysis from observational data, multicriteria optimization of large-scale systems in parallel mode, adopting the PSI method for database searches, and interpretation of the prototype improvement problem. The book also offers guidance in understanding and using the accompanying, newly released MOVI software package.
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and cases studies (Volume 2).The proposed book follows the approach of ?programmed learning? whereby material is presented in short sections called ?frames?. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).
Since the volume may be of interest to a broad variety of people, it is arranged in parts that require different levels of mathematical background. Part I can be assessed by those interested in the application of visualization methods in decision making. In Part II computational methods are introduced in a relatively simple form. Part III is written for readers in applied mathematics interested in the theoretical basis of modern optimization.
The history of physics furnishes many examples of how a simple semiem pirical method, essentially based on intuitive considerations, may prove to be much more successful than a rigorous theoretical approach. A pertinent example is the method of atom-atom potentials, which treats the intermolec ular interactions between polyatomic molecules in terms of pairwise inter actions between their constituent atoms. Despite a few conceptual short comings, the method provides a fairly reliable practical means of handling, on a microscopic level, a wide range of problems that arise in the solid-state physics and chemistry of organic compounds. This monograph is an attempt to generalize the experience gained in the past twenty years in interpreting the static and dynamic properties of organic molecular solids in terms of atom-atom potentials. It embraces nearly all aspects of the application of the method, including an evaluation of cohesive energies, equilibrium crystal structures, phonon spectra, ther modynamic functions, and crystal defects. Many related topics such as the effect of the crystal field on molecular conformation, the determination of crystal structures from raw diffraction data, and the problem of polymor phic transitions are also discussed. We believe that this book will be of use to researchers in solid-state physics, chemistry, crystallography, physical chemistry, and polymer chem istry. It also gives us an opportunity to acknowledge our indebtedness to those who sent us published as well as unpublished information and sugges tions, including A.T. Amos, E.L. Bokhenkov, H. Bonadeo, R.K. Boyd, C.P.
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).
The Parameter Space Investigation (PSI) method was developed to help engineers with a wide range of multicriteria optimization problems, such as design, identification, design of control systems, and operational development of prototypes. This unique resource shows you how to use PSI to construct a feasible solution set without limitations on the number of parameters and criteria. The book presents visualization tools that are used to construct the feasible solution set, conduct multicriteria analysis, and correct the initial problem statement. You explore topics that have not been covered in any other books, including multicriteria analysis from observational data, multicriteria optimization of large-scale systems in parallel mode, adopting the PSI method for database searches, and interpretation of the prototype improvement problem. The book also offers guidance in understanding and using the accompanying, newly released MOVI software package.
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and cases studies (Volume 2).The proposed book follows the approach of ?programmed learning? whereby material is presented in short sections called ?frames?. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.
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