Thesis (Ph. D.)--University of Wisconsin--Madison, 1973. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography: leaves 262-263.

What is the correct way to respond to newly acquired information? What methods for updating beliefs and other attitudes are rational? And what makes them rational? This dissertation is a collection of independent essays, each of which addresses these questions. Among other things, I investigate the extent to which Bayesian learning can be considered objective, the circumstances in which rational learning reduces uncertainty and produces consensus, whether rational learning is compatible with disagreement and polarization, and the relationship between long-run and short-run norms for learning.

Thesis (Ph. D.)--University of Washington, 1985. / Vita. Bibliography: leaves [131]-137.

Thesis (Ph. D.)--University of North Carolina at Greensboro, 2005. / Title from PDF title page screen. Includes bibliographical references (p. 76-93).

Bibliography: leaves 397-406.

This thesis argues that one can fruitfully think of Nelson Goodman's New Riddle of Induction as a reductio ad absurdum of a certain set of views of the relationship between similarities, on the one hand, and properties, concepts, or predicates, on the other. It argues that any view which takes similarities between particulars to be most fundamentally explained by those objects' sharing a property, satisfying a concept, or falling under a predicate leaves itself without the resources to provide a satisfying answer to a Goodmanian sceptic who proposes that inductive inferences using "grue" are equally as warranted as those using "green". I argue for an alternative view of similarity and inductive warrant which holds that the content of perceptual experience includes non-conceptual content the satisfaction conditions of which include that concept-independent similarities obtain. I argue further that it is only on the basis of that non-conceptual content that we are able satisfactorily to distinguish predicates like "grue" from those like "green." We must make such a distinction if we are to provide an acceptable account of inductive warrant. In the course of developing this view, I critique a range of mainstream, contemporary accounts of the relationship between similarities, concepts and properties, and of the role of perceptual experience in justifying empirical beliefs. Chapter 1 argues for a realist view of similarities between particulars which takes our concepts of properties to spring from our observations of those similarities. This view is contrasted with David Armstrong's universal realism, which is rejected. Chapter 2 argues that Goodman's approach to his New Riddle based on entrenchment fails, and argues that if and only if one embraces the view of similarity and concepts that I favor then the New Riddle can be reduced to traditional Humean concerns about induction. Chapters 3 through 5 discuss difficulties for Donald Davidson's approach to the New Riddle, his account of the justification of empirical belief, and his rejection of the very idea of a conceptual scheme, tracing each of these difficulties to Davidson's view that similarities must always be understood in terms of concepts under which particulars fall. Using John McDowell's Mind and World as an example, Chapter 6 argues that any account of perceptual justification of empirical belief according to which the content of perception is limited to conceptual content will fall into the New Riddle, while accounts which permit non-conceptual content can avoid this problem.

Bibliography: leaves 397-406 / 406 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Philosophy, 1986

Thesis (Ph. D.)--University of California, Santa Cruz, 1993. / Typescript. Includes bibliographical references (leaves 75-80).

A damage identification method incorporating the use of inductive learning is presented. Inductive learning is the process of learning from examples. The method utilizes as much dynamic-response data as is available, ordering this information to find the best data with which to discriminate among a set of damage states available for dynamic testing. This method takes into account the inherent variabilities in the damage identification problem. These inherent variabilities include but are not restricted to sensor noise, changes in environmental conditions, slight changes in boundary conditions, and manufacturing differences. The method statistically isolates changes in the dynamic-response characteristics due to damage from these inherent variances. This method is model-independent and can be used to accommodate any sensors, actuators, and data type. In order to demonstrate the method, an experiment was performed on a 12” x 12” x ⅛” aluminum plate hung horizontally from the corners to simulate free-free boundary conditions. The plate was sensed and actuated by two piezoelectric patches mounted diagonally symmetric from one another. A small test mass (2% of the mass of the plate) was placed at four discrete locations, changing the physical properties of the structure. The structural impedance-responses were measured for all of the damage cases for both sensors. This information was processed by the damage identification algorithm to generate rules to which a small amount of data, extracted from a single set of structural impedance-response information, can be applied. The method was able to successfully discriminate all of the damage states from one another as well as to detect the existence of a change in physical properties due to a damaged state of which there was no prior knowledge. / Master of Science

This work discusses the effects of inherent variabilities on the damage identification problem. The goal of damage identification is to detect structural damage before it reaches a level which will detrimentally affect the structure’s performance. Inductive learning is one tool which has been proposed as an effective method to perform damage identification. There are many variabilities which are inherent in damage identification and can cause problems when attempting to detect damage. Temperature fluctuation and manufacturing variability are specifically addressed. Temperature is shown to be a cause-effect variability which has a measurable effect on the damage identification problem. The inductive learning method is altered to accommodate temperature and shown experimentally to be effective in identifying added mass damage at several locations on an aluminum plate. Manufacturing variability is shown to be a non-quantifiable variability. The inductive learning method is shown to be able to accommodate this variability through careful examination of statistical significances in dynamic response data. The method is experimentally shown to be effective in detecting hole damage in randomly selected aluminum plates from a manufactured batch. / Master of Science