Quasi-things: The Paradigm Of Atmospheres

Download Quasi-things: The Paradigm Of Atmospheres

In this book, Tonino Griffero introduces and analyzes an ontological category he terms "quasi-things." These do not exist fully in the traditional sense as substances or events, yet they powerfully act on us and on our states of mind. He offers an original approach to the study of emotions, regarding them not as inner states of the subject, but as atmospheres, that is as powers poured out into the lived space we inhabit. Griffero first outlines the general and atmospheric characters of quasi-things, and then considers examples such as pain, shame, the gaze, and twilight–which he argues is responsible for penetrating and suggestive moods precisely because of its vagueness. With frequent examples from literature and everyday life, Quasi-Things provides an accessible aesthetic and phenomenological account of feelings based on the paradigm of atmospheres.

Support Vector Machine. Examples With Matlab

Download Support Vector Machine. Examples With Matlab

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. This book develops Support Vector Machine techniques.