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Utkir A Rozikov: Gibbs Measures In
Machine Learning

Gibbs Measures In Machine Learning

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming

Using SAS Enterprise Miner

Data Science and Machine Learning for Non-Programmers

Series in Computer Vision

This compendium provides a hands-on description of Random Forests. It starts with a consistent introduction of general methods to create, train, and fuse ensembles of decision trees. Instead of limiting the discussion to the general-purpose layout of traditional Random Forests, this book outlines specifications during tree creation and training that are especially well suited to analyze structured data such as images. The theoretical foundations are explained as well as practical and implementation issues. The many possible variations of the Random Forest model are discussed together with their implications on the prediction results to provide insights into the influence of these parameters and their possible side-effects. Last but not least, this unique title provides specific examples of the usage of Random Forests for analysing remote sensing imagery.

Handbook Of Random Forests: Theory And Applications For Remote Sensing

This book illustrates how the choice of relevant data collection and data analysis techniques is critical for a sound and objective description of the complexity of space-time patterns and processes in ecology and the aquatic sciences. It reviews the concept of complexity in a general ecological context, before introducing a unifying definition of complexity in marine ecology. The book shows that convincing results can arise from very simple, sound, and well-thought-out approaches. The authors underline the risks related to modern techniques of data assimilation that can lead to data inundation, a loss of focus, and ultimately an erroneous scientific approach.

A Practical Guide to Avoid Becoming a Mistaken Scientist

Interpreting Ecological Complexity

Qualitative Sozialforschung

Gemeinsam zu interpretieren gehört in der deutschen qualitativen Sozialforschung seit Jahrzehnten zum Alltag der Forschung – ohne dass allerdings etwas Wesentliches über diesen Alltag der Gruppeninterpretationen bekannt ist. Das vorliegende Buch möchte deshalb die alltägliche Praxis der Gruppeninterpretation&nbsp;erstmalig sichtbar machen und reflektieren. Zu diesem Zweck wird&nbsp;ein systematischer Überblick über die Theorien und Praxen der Gruppeninterpretation und deren Spielarten&nbsp;gegeben. Dann wird ein Fall einer Gruppeninterpretation exemplarisch dokumentiert und reflektiert. Dabei werden vor allem die kommunikativen und sozialen Prozesse bei der Produktion und Konstruktion sozialwissenschaftlichen Wissens sichtbar(er) gemacht. So wird nicht nur das Reflexionswissen über dieses Verfahren gemehrt, sondern dieses Wissen ist hilfreich, wenn man selbst Gruppeninterpretationen&nbsp;durchführen oder daran teilnehmen möchte. Der Inhalt Gemeinsam Interpretieren als Kommunikationsprozess • Über den Alltag der gemeinsamen Auslegung • Wissenssoziologische Bildanalyse • Transkription einer Interpretationssitzung • Diese Familie ist Amerika! Eine hermeneutisch-wissenssoziologische Interpretation&nbsp;Die Zielgruppe Studierende und Dozierende aller Sozialwissenschaften, die sich mit Methoden der qualitativen Sozialforschung beschäftigen. Der Autor Dr. Jo Reichertz ist Professor für Kommunikationswissenschaft an der Universität Duisburg-Essen.&nbsp;

Die Gruppeninterpretation als kommunikativer Prozess

Gemeinsam interpretieren

Meta-analysis Using R

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

This book presents an overview of the diverse literature on change-point methodology and describes some unifying themes and recent breakthroughs. It can be used as a second-year Ph.D. level course in statistics. It is expected to be useful to researchers and graduate students, not only in statistics but also in engineering, biomedicine, economics and finance.

Change-point Methodology and Applications

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Likelihood and its Extensions

Dynamics and Astrophysics of Galaxies

How Intelligence Arises in People and Machines

The Emergent Mind

Algorithms have become the key organizer through which power is enacted in our society. A huge amount of data regarding our daily routines are monitored and analyzed to make recommendations that manage, control, and lead our behaviors in everyday life. AI, Humans, and Misinformation: How Does AI Alter Human Behavior and How Do Humans Influence Algorithmic Misinformation? is a guide to understanding the dynamics of AI and misinformation in human contexts by addressing meaningful questions-How does AI alter human behavior and how do humans influence algorithmic decision-making? In answering these questions, this book examines the role of misinformation, disinformation, and fake news, and shows readers how to develop AI methods and algorithms that combat misinformation by using AI design choices that provide users and developers alike with meaningful control over AI. This book brings together various perspectives on algorithms into an integrated conceptual framework, and provides a broad socio-technical analysis, addressing critical and ethical issues of misinformation and fake news. The book offers a compelling insight into the misinformation phenomenon and the future of AI-based society. Readers will find an integrated technical analysis of the logic and social implications of algorithmic processes. Reporting from the cutting edge of critical technical methods and research, the result is useful and constructive for developing the relations between algorithms and humans. This is an imperative methodology for understanding what is at stake as industry and government use AI to reshape the world.

Gibbs Measures In Machine Learning

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