Features

Features are a central concept in linguistic analysis. They are the basic building blocks of linguistic units, such as words. For many linguists they offer the most revealing way to explore the nature of language. Familiar features are Number (singular, plural, dual, ...), Person (1st, 2nd, 3rd) and Tense (present, past, ...). Features have a major role in contemporary linguistics, from the most abstract theorizing to the most applied computational applications, yet little is firmly established about their status. They are used, but are little discussed and poorly understood. In this unique work, Corbett brings together two lines of research: how features vary between languages and how they work. As a result, the book is of great value to the broad range of perspectives of those who are interested in language.

Wh-movement and the theory of feature-checking argues that cross-linguistic variation in wh-constructions reduces to the availability of different lexical instantiations of a +wh C0 both across languages and within a single language, and the way in which such lexical elements are syntactically identified, either via movement or base-generation. Evidence from a wide range of patterns including wh-expletive questions leads to the conclusion that wh-feature checking may sometimes be effected non-locally and 'at a distance' (long-distance wh-agreement), and that movement in general takes place for two related but discrete reasons: both to identify and activate an underspecified licensing head and in order for an element to occur in the checking domain projected by its relevant licensing head. Developing and generalizing the proposals beyond wh-phenomena, the study also goes on to argue for a Minimalist model of syntax in which feature-dependencies are in fact all licensed in the overt syntax and where there is no need for any further level of LF.

Well known researchers in all areas related to featured based manufacturing have contributed chapters to this book. Some of the chapters are surveys, while others review a specific technique. All contributions, including those from the editors, were thoroughly refereed. The goal of the book is to provide a comprehensive picture of the present stage of development of Features Technology from the point of view of applications in manufacturing. The book is aimed at several audiences. Firstly, it provides the research community with an overview of the present state-of-the-art features in manufacturing, along with references in the literature. Secondly, the book will be useful as supporting material for a graduate-level course on product modeling and realization. Finally, the book will also be valuable to industrial companies who are assessing the significance of features for their business.

How much of the world's water is found in the oceans? How many volcanoes erupt each year? How was the Grand Canyon formed? Read this book to find out! Part of World Book's Learning Ladders series, this book tells children about different kinds of landforms and how they shape Earth. Children also learn about bodies of water and their importance to people. Each spread includes introductory text, colorful illustrations with detailed captions, and photographs that show real-world examples of the featured topic. Puzzle pages, fun facts, and true/false quizzes appear at the end of each volume.

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.

This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.

Examines unusual animal facial features and how they help the animals survive.