Download Classify And Label full books in PDF, epub, and Kindle. Read online free Classify And Label ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Matt L. Drabek |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2016-05-05 |
| Genre | : Classification |
| ISBN | : 9781498504447 |
Classify and Label is a philosophical treatment of classification in the social sciences and everyday life, focusing on its moral, social, and political implications. This book stands at the intersection of philosophy of the social sciences, feminist philosophy, philosophy of ...
| Author | : Francisco Herrera |
| Publisher | : Springer |
| Total Pages | : 200 |
| Release | : 2016-08-09 |
| Genre | : Computers |
| ISBN | : 331941111X |
This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
| Author | : Matt L. Drabek |
| Publisher | : Lexington Books |
| Total Pages | : 165 |
| Release | : 2014-10-15 |
| Genre | : Philosophy |
| ISBN | : 0739179764 |
Classify and Label: The Unintended Marginalization of Social Groups is a philosophical treatment of classification in the social sciences and everyday life, focusing on moral, social, and political implications. The use of labels is essential to how people navigate and understand the world. Classifications and labels also have a dark side, as they may unintentionally misrepresent groups and individuals. These misrepresentations disrupt how people think about themselves and how they treat others, sometimes leading to marginalization. Matt L. Drabek analyzes classification by considering rich case studies across a variety of domains, including the classification of gender and sexual orientation, the psychiatric classification of sadomasochism and gender disorders, and the classification of people in everyday life through the production of pornography and use of gender identities. This broad sample reveals deep connections between the classifications proposed by social scientists and the classifications used by society at large. Drabek explores how classifications evolve from and eventually affect such seemingly disconnected issues as the situation of under-represented groups in academia, new models of parenting and the family, the nature of sexual orientation, and the nature of scientific bias.
| Author | : Wray Buntine |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 787 |
| Release | : 2009-09-03 |
| Genre | : Computers |
| ISBN | : 3642041736 |
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
| Author | : Steven Bird |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 506 |
| Release | : 2009-06-12 |
| Genre | : Computers |
| ISBN | : 0596555717 |
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
| Author | : Robert J. Glushko |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 743 |
| Release | : 2014-08-25 |
| Genre | : Computers |
| ISBN | : 1491911719 |
Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the "Internet of Things," and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead.
| Author | : Peter Flach |
| Publisher | : Cambridge University Press |
| Total Pages | : 415 |
| Release | : 2012-09-20 |
| Genre | : Computers |
| ISBN | : 1107096391 |
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
| Author | : |
| Publisher | : Columbia Books. Incorporated |
| Total Pages | : 532 |
| Release | : 2015 |
| Genre | : Medical |
| ISBN | : |
The Globally Harmonized System of Classification and Labelling of Chemicals (GHS) addresses classification and labelling of chemicals by types of hazards. It provides the basis for worldwide harmonization of rules and regulations on chemicals and aims at enhancing the protection of human health and the environment during their handling, transport and use by ensuring that the information about their physical, health and environmental hazards is available. The sixth revised edition includes, inter alia, a new hazard class for desensitized explosives and a new hazard category for pyrophoric gases; miscellaneous amendments intended to further clarify the criteria for some hazard classes (explosives, specific target organ toxicity following single exposure, aspiration hazard, and hazardous to the aquatic environment) and to complement the information to be included in section 9 of the Safety Data Sheet; revised and further rationalized precautionary statements; and an example of labelling of a small packaging in Annex 7.
| Author | : Sowmya Vajjala |
| Publisher | : O'Reilly Media |
| Total Pages | : 455 |
| Release | : 2020-06-17 |
| Genre | : Computers |
| ISBN | : 149205402X |
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
| Author | : Thorsten Joachims |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 218 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461509076 |
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
