Big Data Analysis Using Machine Learning for Social Scientists and Criminologists, Song Juyoung

Автор: Cross, Noel
Название: Criminal Law for Criminologists
ISBN: 1138606901 ISBN-13(EAN): 9781138606906
Издательство: Taylor&Francis
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Цена: 148010.00 T
Наличие на складе: Невозможна поставка.
Описание: Criminal Law for Criminologists is an introduction to criminal law that uses theoretical and practical research to bridge the gap between `the law in the books` (criminal law doctrine) and `the law in action` (criminal justice process).

Автор: Cross, Noel
Название: Criminal Law for Criminologists
ISBN: 113860691X ISBN-13(EAN): 9781138606913
Издательство: Taylor&Francis
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Цена: 39800.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Criminal Law for Criminologists is an introduction to criminal law that uses theoretical and practical research to bridge the gap between `the law in the books` (criminal law doctrine) and `the law in action` (criminal justice process).

Автор: Forst
Название: Criminologists on Terrorism and Homeland Security
ISBN: 0521899451 ISBN-13(EAN): 9780521899451
Издательство: Cambridge Academ
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Цена: 126720.00 T
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Описание: This volume presents 19 original essays addressing what is widely regarded as the most serious problem confronting America today and for years to come - terrorism - from the unique perspective of criminology. Criminologists on Terrorism and Homeland Security will be of interest to anyone concerned about violence prevention and terrorism.

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735024 ISBN-13(EAN): 9781681735023
Издательство: Mare Nostrum (Eurospan)
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Цена: 57290.00 T
Наличие на складе: Невозможна поставка.
Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735040 ISBN-13(EAN): 9781681735047
Издательство: Mare Nostrum (Eurospan)
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Цена: 77610.00 T
Наличие на складе: Невозможна поставка.
Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Автор: Lynch Orla, Ahmed Yasmine, Russell Helen
Название: Reflections on Irish Criminology: Conversations with Criminologists
ISBN: 3030605922 ISBN-13(EAN): 9783030605926
Издательство: Springer
Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores the development of the discipline of Criminology on the island of Ireland, through conversations with leading criminologists.

Автор: Subasi, Abdulhamit
Название: Practical Machine Learning For Data Analysis Using Python
ISBN: 0128213795 ISBN-13(EAN): 9780128213797
Издательство: Elsevier Science
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Цена: 110030.00 T
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Описание:

Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.