Nitel ve Nicel Araştırma Tasarımı Uygulamasıyla Etanol Kullanımını ve Etkilerinin Anlaşılması

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. Show more

Introduction As a research methodology, phenomenology is uniquely positioned to help health professions education (HPE) scholars learn from the experiences of others. Phenomenology is a form of qualitative research that focuses on the study of an individual’s lived experiences within the world. Although it is a powerful approach for inquiry, the nature of this methodology is often intimidating to HPE researchers. This article aims to explain phenomenology by reviewing the key philosophical and methodological differences between two of the major approaches to phenomenology: transcendental and hermeneutic. Understanding the ontological and epistemological assumptions underpinning these approaches is essential for successfully conducting phenomenological research. Purpose This review provides an introduction to phenomenology and demonstrates how it can be applied to HPE research. We illustrate the two main sub-types of phenomenology and detail their ontological, epistemological, and methodological differences. Conclusions Phenomenology is a powerful research strategy that is well suited for exploring challenging problems in HPE. By building a better understanding of the nature of phenomenology and working to ensure proper alignment between the specific research question and the researcher’s underlying philosophy, we hope to encourage HPE scholars to consider its utility when addressing their research questions. Show more