Syntactic methods in pattern recognition

Edición de la obra Syntactic methods in pattern recognition

In this book, we study theoretical and practical aspects of computing methods for mathematical modeling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. Topics include best operator approximation, non-Lagrange interpolation, generic Karhunen-Loeve transform, generalized low-rank matrix approximation, optimal data compression, and optimal nonlinear filtering.