Nitin Indurkhya – författare & böcker

Text Mining

Predictive Methods for Analyzing Unstructured Information

E-bok

PDF, Engelska, 2010

2 110 kr

Läs direkt efter köp

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Text Mining

Predictive Methods for Analyzing Unstructured Information

AvSholom M. Weiss,Nitin Indurkhyam. fl.

Inbunden, Engelska, 2004

1 664 kr

Skickas inom 10-15 vardagar

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Rise of Intelligent Machines

A Multi-disciplinary Perspective from Industry and Impact on Higher Education

AvNadya Shaznay Patel,Nitin Indurkhya

Häftad, Engelska, 2025

826 kr

Skickas inom 10-15 vardagar

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI's transformative potential and the strategies needed to harness its benefits for industry and education.Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.Cover design by Ernest Tey

Rise of Intelligent Machines

A Multi-disciplinary Perspective from Industry and Impact on Higher Education

AvNadya Shaznay Patel,Nitin Indurkhya

Inbunden, Engelska, 2025

1 999 kr

Skickas inom 10-15 vardagar

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI's transformative potential and the strategies needed to harness its benefits for industry and education.Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.Cover design by Ernest Tey

Handbook of Natural Language Processing

AvNitin Indurkhya,Fred J. Damerau

E-bok

Engelska, 2010

1 022 kr

Läs direkt efter köp

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater

Rise of Intelligent Machines

A Multi-disciplinary Perspective from Industry and Impact on Higher Education

AvNitin Indurkhya,Nadya Shaznay Patel

E-bok

PDF, Engelska, 2025

980 kr

Läs direkt efter köp

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.

Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.

As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI''s transformative potential and the strategies needed to harness its benefits for industry and education.

Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.

Cover design by Ernest Tey

Rise of Intelligent Machines

A Multi-disciplinary Perspective from Industry and Impact on Higher Education

AvNitin Indurkhya,Nadya Shaznay Patel

E-bok

Engelska, 2025

980 kr

Läs direkt efter köp

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.

Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.

As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI''s transformative potential and the strategies needed to harness its benefits for industry and education.

Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.

Cover design by Ernest Tey

Handbook of Natural Language Processing

AvNitin Indurkhya,Fred J. Damerau

Inbunden, Engelska, 2010

2 494 kr

Skickas inom 10-15 vardagar

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second Edition Greater prominence of statistical approachesNew applications sectionBroader multilingual scope to include Asian and European languages, along with EnglishAn actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developmentsDivided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

Handbook of Natural Language Processing

AvNitin Indurkhya,Fred J. Damerau

E-bok

PDF, Engelska, 2010

1 022 kr

Läs direkt efter köp

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater

Text Mining

Predictive Methods for Analyzing Unstructured Information

AvSholom M. Weiss,Nitin Indurkhyam. fl.

Häftad, Engelska, 2010

1 664 kr

Skickas inom 10-15 vardagar

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Fundamentals of Predictive Text Mining

AvSholom M. Weiss,Nitin Indurkhyam. fl.

Inbunden, Engelska, 2015

758 kr

Skickas inom 10-15 vardagar

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

Fundamentals of Predictive Text Mining

AvTong Zhang,Nitin Indurkhyam. fl.

E-bok

PDF, Engelska, 2015

734 kr

Läs direkt efter köp

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

Fundamentals of Predictive Text Mining

AvSholom M. Weiss,Nitin Indurkhyam. fl.

Häftad, Engelska, 2016

559 kr

Skickas inom 10-15 vardagar

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

Fundamentals of Predictive Text Mining

AvTong Zhang,Nitin Indurkhyam. fl.

E-bok

PDF, Engelska, 2010

652 kr

Läs direkt efter köp

One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents.Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers.Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material.Fundamentals of Predictive Text Mining is an essential resource for IT professionalsand managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.

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Nitin Indurkhya – författare

Text Mining

Text Mining

Rise of Intelligent Machines

Rise of Intelligent Machines

Handbook of Natural Language Processing

Rise of Intelligent Machines

Rise of Intelligent Machines

Handbook of Natural Language Processing

Handbook of Natural Language Processing

Text Mining

Fundamentals of Predictive Text Mining

Fundamentals of Predictive Text Mining

Fundamentals of Predictive Text Mining

Fundamentals of Predictive Text Mining

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