Classify And Label PDF Download
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| Author | : Matt L. Drabek |
| Publisher | : Lexington Books |
| Total Pages | : 165 |
| Release | : 2014-10-15 |
| Genre | : Philosophy |
| ISBN | : 0739179764 |
Download Classify and Label Book in PDF, ePub and Kindle
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 | : Francisco Herrera |
| Publisher | : Springer |
| Total Pages | : 200 |
| Release | : 2016-08-09 |
| Genre | : Computers |
| ISBN | : 331941111X |
Download Multilabel Classification Book in PDF, ePub and Kindle
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 | : Honghua Dai |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 731 |
| Release | : 2004-05-11 |
| Genre | : Business & Economics |
| ISBN | : 354022064X |
Download Advances in Knowledge Discovery and Data Mining Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining.
| Author | : Steven Bird |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 506 |
| Release | : 2009-06-12 |
| Genre | : Computers |
| ISBN | : 0596555717 |
Download Natural Language Processing with Python Book in PDF, ePub and Kindle
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 | : Peter Flach |
| Publisher | : Cambridge University Press |
| Total Pages | : 415 |
| Release | : 2012-09-20 |
| Genre | : Computers |
| ISBN | : 1107096391 |
Download Machine Learning Book in PDF, ePub and Kindle
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
| Author | : Robert J. Glushko |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 743 |
| Release | : 2014-08-25 |
| Genre | : Computers |
| ISBN | : 1491911719 |
Download The Discipline of Organizing: Professional Edition Book in PDF, ePub and Kindle
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 | : Wei Bi |
| Publisher | : |
| Total Pages | : 130 |
| Release | : 2015 |
| Genre | : Classification |
| ISBN | : |
Download Multilabel Classification with Label Structures Book in PDF, ePub and Kindle
| Author | : Sowmya Vajjala |
| Publisher | : O'Reilly Media |
| Total Pages | : 455 |
| Release | : 2020-06-17 |
| Genre | : Computers |
| ISBN | : 149205402X |
Download Practical Natural Language Processing Book in PDF, ePub and Kindle
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 | : Walter Daelemans |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 714 |
| Release | : 2008-09-04 |
| Genre | : Computers |
| ISBN | : 354087478X |
Download Machine Learning and Knowledge Discovery in Databases Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 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 | : Derrall L. Heath |
| Publisher | : |
| Total Pages | : 41 |
| Release | : 2011 |
| Genre | : Electronic dissertations |
| ISBN | : |
Download Improving Multi-label Classification by Avoiding Implicit Negativity with Incomplete Data Book in PDF, ePub and Kindle
Many real world problems require multi-label classification, in which each training instance is associated with a set of labels. There are many existing learning algorithms for multi-label classification; however, these algorithms assume implicit negativity, where missing labels in the training data are automatically assumed to be negative. Additionally, many of the existing algorithms do not handle incremental learning in which new labels could be encountered later in the learning process. A novel multi-label adaptation of the backpropagation algorithm is proposed that does not assume implicit negativity. In addition, this algorithm can, using a naive Bayesian approach, infer missing labels in the training data. This algorithm can also be trained incrementally as it dynamically considers new labels. This solution is compared with existing multi-label algorithms using data sets from multiple domains and the performance is measured with standard multi-label evaluation metrics. It is shown that our algorithm improves classification performance for all metrics by an overall average of 7.4% when at least 40% of the labels are missing from the training data, and improves by 18.4% when at least 90% of the labels are missing.
