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Behavior analysis with machine learning using R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Behavior analysis with machine learning using REnrique Garcia Ceja.
Author:	Garcia Ceja, Enrique.
Published:	Boca Raton, FL :CRC Press,2022.
Description:	1 online resource (xxxiv, 400 p.)
Notes:	"A Chapman & Hall book."
Subject:	Behavioral assessmentData processing.
Online resource:	https://www.taylorfrancis.com/books/9781003203469
ISBN:	9781003203469$q(ebook)

Behavior analysis with machine learning using R
Garcia Ceja, Enrique.

Behavior analysis with machine learning using R[electronic resource] /Enrique Garcia Ceja. - 1st ed. - Boca Raton, FL :CRC Press,2022. - 1 online resource (xxxiv, 400 p.) - Chapman & Hall/CRC the R series. - Chapman & Hall/CRC the R series (CRC Press).

"A Chapman & Hall book."

Includes bibliographical references and index.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

ISBN: 9781003203469$q(ebook)Subjects--Topical Terms:

561560
Behavioral assessment
--Data processing.

LC Class. No.: BF176.2

Dewey Class. No.: 155.2/8

Behavior analysis with machine learning using R
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245 1 0 $a Behavior analysis with machine learning using R $h [electronic resource] / $c Enrique Garcia Ceja.
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500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references and index.
520 $a Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
588 $a Description based on print version record.
650 0 $a Behavioral assessment $x Data processing. $3 561560
650 0 $a Task analysis $x Data processing. $3 960208
650 0 $a Machine learning. $3 188639
650 0 $a R (Computer program language) $3 210846
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