Feature Engineering for Machine Learning and Data Analytics
Feature Engineering for Machine Learning and Data Analytics

Author: Guozhu Dong

Publisher: CRC Press

Published: 2018-03-14

Total Pages: 419

ISBN-13: 1351721275

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Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Computational Science - ICCS 2006
Computational Science - ICCS 2006

Author: Vassil N. Alexandrov

Publisher: Springer

Published: 2006-05-10

Total Pages: 1169

ISBN-13: 3540343849

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This is Volume III of the four-volume set LNCS 3991-3994 constituting the refereed proceedings of the 6th International Conference on Computational Science, ICCS 2006. The 98 revised full papers and 29 revised poster papers of the main track presented together with 500 accepted workshop papers were carefully reviewed and selected for inclusion in the four volumes. The coverage spans the whole range of computational science.

Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications
Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications

Author: Long Jin

Publisher: Frontiers Media SA

Published: 2024-07-24

Total Pages: 301

ISBN-13: 2832552013

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Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.

Agents for Games and Simulations II
Agents for Games and Simulations II

Author: F. Dignum

Publisher: Springer Science & Business Media

Published: 2011-01-14

Total Pages: 216

ISBN-13: 3642181805

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While today's game engines and multi-agent platforms cross-fertilize each other to some extent, the technologies used in these areas are not readily compatible due to some differences in their primary concerns. Where game engines prioritize efficiency and central control, multi-agent platforms focus on agent autonomy and sophisticated communication capabilities. This volume gives an overview of the current state of the art for people wishing to combine agent technology with (serious) games. This state-of-the-art survey contains a collection of papers presented at AGS 2010; the Second International Workshop on Agents for Games and Simulations, held on May 10, 2010, in Toronto, as well as extended versions of papers from other workshops and from the AAMAS conference. The 14 papers are organized in three topical sections focusing on architectures combining agents and game engines, on the training aspects of the games, on social and organizational aspects of games and agents, respectively.

Model-Based Reinforcement Learning
Model-Based Reinforcement Learning

Author: Milad Farsi

Publisher: John Wiley & Sons

Published: 2022-12-02

Total Pages: 276

ISBN-13: 1119808596

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Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

A Research Agenda for Spatial Analysis
A Research Agenda for Spatial Analysis

Author: Levi John Wolf

Publisher: Edward Elgar Publishing

Published: 2024-05-02

Total Pages: 253

ISBN-13: 1802203230

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This Research Agenda explores the future of spatial analysis, and how the field informs and challenges the policy landscape. A wide range of contributors from different intellectual communities address the problem of causality in geographic analysis, arguing that diversity is crucial for the future success of the discipline. This title contains one or more Open Access chapters.