Bücher von Enrique Castillo

A general introduction to expert systems dealing with uncertainty and learning methods; describing the most common methods and pointing out their deficiencies.

Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected.

The concept of conditional specification is not new. Arnold Riverside, California USA Enrique Castillo Jose Maria Sarabia Santander, Cantabria Spain January, 1991 Contents 1 Conditional Specification 1 1.1 Why? . 2 1.3 Early work on conditional specification 4 1.4 Organization of this monograph . . . . . . . . . . . . . . . . . . . . . . . . . . .

Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.

Presents a practical approach to decomposition techniques in optimization. This book addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides decomposition algorithms as well as heuristic ones.

Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected.

Efforts to visualize multivariate densities necessarily involve the use of cross-sections, or, equivalently, conditional densities. All statistical researchers seeking more flexible models than those provided by classical models will find conditionally specified distributions of interest.