Originally published in 1973, this book was published in two volumes. In the first volume, the author describes what he sees as the rudiments of three deterministic partial theories of structural learning. The first involves competence, partial theories which deal only with the problem of how to account for the various kinds of behavior of which people are typically capable. Special attention is given to mathematical competence. Nothing is said about learning or performance. The second partial theory is concerned with motivation, learning, and performance under idealized conditions, and is obtained from the first partial theory by imposing further structure on it. This theory says nothing about memory of the limited capacity of human subjects to process information. ... The final theory is obtained from the second by making additional assumptions, which bring memory and finite information processing into the picture. The theory is still partial, however, since no attempt is made to deal with certain ultra-short-term behavioral phenomena which appear to depend directly on particular physiological characteristics.

Originally published in 1976, this title is an edited volume and reflects the major approaches being taken in structural learning at the time. Chapter 1 deals with the basic question of whether competence (knowledge) should be characterized in terms of rules (automata), on the one hand, or associations on the other. The bulk of Chapter 2 is devoted to a series of earlier experiments on rule learning by the editor and his associates. The two contributions in Chapter 3 deal with graph theoretical models. Piagetian models constitute the subject of Chapter 4. Chapter 5 deals with attempts to stimulate human behaviour with a computer. Chapter 6 ranges over a wide variety of competence models, with particular reference to logic and mathematics. In Chapter 7 the editor proposes a new theory of structural learning, together with some empirical results.

Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.

Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

For the thousands of students who apply every year for one of the four military academies, slogging through the numbing concatenation of decisions preceding a nomination, there is no greater intimidation than the statistically likely event that they will try and fail. Thats an examination into the pithiness of moral fiber important to the USNA, and eulogized by James Stockdale, USNA 46 and Medal of Honor Winner: "The test of character is not 'hanging in there' when you expect a light at the end of the tunnel, but performance of duty and persistence of example when you know that no light is coming. This is the true story of how one All-American kidlike those many that applydid it. She had no idea she could aim so high and succeed so succinctly. Her research into the typical Midshipman uncovered a profile alarmingly like herself. If she dreamt of attending a college where she fit in and attracted kindred souls, this qualified. When you first meet Meaghan, you may wonder, why does she think an Ivy League school will accept her? She doesnt earn straight As or play quarterback on the football teamor center on the volleyball squad. I describe in detail her background, her academic interests, her focus, as well as her struggle to put together a winning admissions package. Along the way, you gain insight into the moral fiber that grounds everything she does and allows her to fight the good fight. The support from family and friends, and decisions she must make that superficially appear impossible for an adolescent, but are in fact achievable for thousands of like-minded teens.

Instructional Design Theories and Models is a thorough yet concise overview of eight of the most comprehensive and best-known attempts to integrate knowledge about effective and appealing instruction. Chapters were written by the original theorists to provide a more accurate and behind-the-scenes look at the theories' development. Instructional Des