Automated Taxonomy Discovery and Exploration, Shen
Автор: Hovy Dirk Название: Text Analysis in Python for Social Scientists ISBN: 1108819826 ISBN-13(EAN): 9781108819824 Издательство: Cambridge Academ Рейтинг: Цена: 19010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Text is a fantastic resource for social scientists, but because it is so abundant, and so variable, it can be difficult to extract the information we want. Many basic text analysis methods are available as Python implementations: this Element will teach you when to use which method, how it works, and the Python code to implement it.
Автор: Bissett Название: Automated Data Analysis Using Excel, Second Edition ISBN: 1482250136 ISBN-13(EAN): 9781482250138 Издательство: Taylor&Francis Рейтинг: Цена: 69410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This new edition includes some key topics relating to the latest version of MS Office, including use of the ribbon, current Excel file types, Dashboard, and basic Sharepoint integration. It shows how to automate operations, such as curve fitting, sorting, filtering, and analyzing data from a variety of sources.
Автор: Angermann Название: Taxonomy Matching Using Background Knowledge ISBN: 3319722085 ISBN-13(EAN): 9783319722085 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches.
Автор: Tao Li; Mitsunori Ogihara; George Tzanetakis Название: Music Data Mining ISBN: 1439835527 ISBN-13(EAN): 9781439835524 Издательство: Taylor&Francis Рейтинг: Цена: 112290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing.
The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining.
The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734575 ISBN-13(EAN): 9781681734576 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 87780.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734559 ISBN-13(EAN): 9781681734552 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Mike Thelwall Название: Word Association Thematic Analysis: A Social Media Text Exploration Strategy ISBN: 163639065X ISBN-13(EAN): 9781636390659 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 48050.00 T Наличие на складе: Нет в наличии. Описание: This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts.
Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection.
The method starts by automatically finding words that are statistically significantly more common in one subset than another, then identifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages.
Автор: Mike Thelwall Название: Word Association Thematic Analysis: A Social Media Text Exploration Strategy ISBN: 1636390676 ISBN-13(EAN): 9781636390673 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 69300.00 T Наличие на складе: Нет в наличии. Описание: This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts.
Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection.
The method starts by automatically finding words that are statistically significantly more common in one subset than another, then identifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages.
Автор: Ganter Bernhard, Obiedkov Sergei Название: Conceptual Exploration ISBN: 366256999X ISBN-13(EAN): 9783662569993 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Generalizations that handle incomplete, faulty, orimprecise data are discussed, but the focus lies on knowledge extraction from areliable information source.The method is based on Formal Concept Analysis, a mathematical theory ofconcepts and concept hierarchies, and uses its expressive diagrams.
Автор: Dube Simant Название: An Intuitive Exploration of Artificial Intelligence: Theory and Applications of Deep Learning ISBN: 303068623X ISBN-13(EAN): 9783030686239 Издательство: Springer Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Part I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.
Автор: Dube, Simant Название: Intuitive exploration of artificial intelligence ISBN: 3030686264 ISBN-13(EAN): 9783030686260 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Автор: Chbeir Название: Mining Intelligence and Knowledge Exploration ISBN: 3031215168 ISBN-13(EAN): 9783031215162 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes revised selected papers from the refereed proceedings of the 9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021, which took place in Hammamet, Tunisia, in November 2021. The 22 full papers included in this book were carefully reviewed and selected from 61 submissions. They deal with topics such as evolutionary computation, knowledge exploration in IoT, artificial intelligence, machine learning, data mining and information retrieval, medical image analysis, pattern recognition and computer vision, speech / signal processing, text mining and natural language processing, intelligent security systems, Smart and Intelligent Systems, etc.
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