Suchergebnisse

The need for a more and more comprehensive classification system of mental disorders that responds to the difficulties of the subject is more imperative than ever. The purpose of this study through a critical look is to highlight the way a diagnostic system "thinks" about the symptoms of each subject. Methodologically, in the context of the modern international literature, an attempt is made to capture perceptions from the field of scientific knowledge regarding psychodiagnostic systems that dominate the mental health field. More specifically, it reflects the way in which each psychodiagnostic system classifies symptoms in the context of a phenomenology of things, leaving aside the personal pain and the uniqueness of each subject. In conclusion, it seems that psychodiagnostic systems talk about symptoms that are divided into clusters and each of them gets a name. They don't talk about causes, they don't take into account the difference, nor the environment and the temperament of each person, as a result of which difficulties are created because they do not aim therapeutically at what gives birth to the symptom, but at what each symptom shows.

AbstractThere is no universally accepted methodology to determine how much confidence one should place in the output of a classification system. In this article, we develop a confidence paradigm. This is a theoretical framework that attempts to unite the viewpoints of the classification system developer (or engineer) and the classification system user (or warfighter). The developer designs and tests the classification system at a macro‐level. The user fields the system in an environment often quite different than the environment used to develop the system. The user operates at a micro‐level and is interested in the indications as they are made by the system. The paradigm is based on the assumptions that the system confidence acts like or can be modelled as value, and that indication confidence can be modelled as a function of the posterior probability estimates. The viewpoints of the developer and the user are unified through the fundamental proposition that the expected value of the user's confidence should be approximately equal to the developer's confidence. This paradigm provides a direct link between traditional decision analysis techniques and traditional pattern recognition techniques. This methodology is applied to an automatic target recognition data set, and the results demonstrate the sort of behavior that would be expected from a rational confidence measure. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

For decades, efforts have been made to promote uniform classification systems of mental disorders, to enhance research and consistent treatment. This article discusses the controversies regarding the influential U.S. manual, the Diagnostic and Statistical Manual of Mental Disorders and critiques the popular codependence-addiction movement, both of which discriminate against women and ignore the possibility of social change. It presents a nonstigmatizing therapeutic assessment and programming model, the psychosocial spectrum model, and examples of relevant components of successful women's programs throughout the world.

Tese de Doutorado, defendida na Université Du Québec, Canadian. 2010 ; An incremental learning system updates itself in response to incoming data without reexamining all the old data. Since classification systems capable of incrementally storing, filtering, and classifying data are economical, in terms of both space and time, which makes them immensely useful for industrial, military, and commercial purposes, interest in designing them is growing. However, the challenge with incremental learning is that classification tasks can no longer be seen as unvarying, since they can actually change with the evolution of the data. These changes in turn cause dynamic changes to occur in the classification system's parameters If such variations are neglected, the overall performance of these systems will be compromised in the future. In this thesis, on the development of a system capable of incrementally accommodating new data and dynamically tracking new optimum system parameters for self-adaptation, we first address the optimum selection of classifiers over time. We propose a framework which combines the power of Swarm Intelligence Theory and the conventional grid-search method to progressively identify potential solutions for gradually updating training datasets. The key here is to consider the adjustment of classifier parameters as a dynamic optimization problem that depends on the data available. Specifically, it has been shown that, if the intention is to build efficient Support Vector Machine (SVM) classifiers from sources that provide data gradually and serially, then the best way to do this is to consider model selection as a dynamic process which can evolve and change over time. This means that a number of solutions are required, depending on the knowledge available about the problem and uncertainties in the data. We also investigate measures for evaluating and selecting classifier ensembles composed of SVM classifiers. The measures employed are based on two different theories (diversity and margin) commonly used to understand the success of ensembles. This study has given us valuable insights and helped us to establish confidence-based measures as a tool for the selection of classifier ensembles. The main contribution of this thesis is a dynamic optimization approach that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses over time. The approach incorporates various theories, such as dynamic Particle Swarm Optimization, incremental Support Vector Machine classifiers, change detection, and dynamic ensemble selection based on classifier confidence levels. Experiments carried out on synthetic and real-world databases demonstrate that the proposed approach outperforms the classification methods often used in incremental learning scenarios.

Disability classification systems belong to the core of states' social/disability policies through which persons with disabilities are classified as eligible or ineligible for having access to disability allowances. The study of disability classification systems has stimulated the interest of several scholars from the broader area of disability studies. Either by conducting comparative studies between different states and describing the similarities and differences of these systems around the world or by conducting studies focusing on the politics and semantics in the development of disability classification systems in specific states, all studies have shown a pluralism in the systems for assessing and certifying disability. In Greece, the development of disability classification systems for social welfare reasons emerged as a controversy that lasted for almost twenty years. One factor that strengthened the controversy was the outbreak of the economic crisis late in 2009 followed by the announcement by the governmental authorities of the enactment of a new system for assessing and certifying disability as part of the austeritydriven policies that the Greek state would enact for facing the consequences of the economic crisis. Drawing on an interdisciplinary approach, the overall aim of this study is to describe and analyze the enactment of disability classification systems in the context of Greek social policy from 1990 to 2015. For the collection of empirical material, a qualitative research method was employed, consisting of interviews, written material, and newspaper articles. The main findings of this thesis are: I) the involvement of the political parties in the development of the systems for certifying and assessing disability; II) the involvement of the disability movement in policymaking; III) the "creative" use of statistics by governmental authorities for the enactment of disability/social policies; IV) how the concept of "disability fraud" has been constructed as a "threat" to the society; and V) the vulnerability of disability classification systems in times of austerity.