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Behavior analysis with machine learning using R

Behavior analysis with machine learning using R (1회 대출)

자료유형
단행본
개인저자
Garcia Ceja, Enrique, author.
서명 / 저자사항
Behavior analysis with machine learning using R / Enrique Garcia Ceja.
발행사항
London ;   Boca Raton :   CRC Press,   2022.  
형태사항
xxxiii, 397 p. : ill. (some col.) ; 25 cm.
총서사항
The R series
ISBN
9781032067049 (hardback) 9781032067056 (paperback) 9781003203469 (ebook)
요약
"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"--
내용주기
Introduction to behavior and machine learning -- Predicting behavior with classification models -- Predicting behavior with ensemble learning -- Exploring and visualizing behavioral data -- Preprocessing behavioral data -- Discovering behaviors with unsupervised learning -- Encoding behavioral data -- Predicting behavior with deep learning -- Multi-user validation -- Detecting abnormal behaviors.
서지주기
Includes bibliographical references (p. 387-394) and index.
일반주제명
Behavioral assessment --Data processing. Task analysis --Data processing. Machine learning. R (Computer program language).
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020 ▼a 9781032067056 (paperback)
020 ▼a 9781003203469 (ebook)
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100 1 ▼a Garcia Ceja, Enrique, ▼e author.
245 1 0 ▼a Behavior analysis with machine learning using R / ▼c Enrique Garcia Ceja.
260 ▼a London ; ▼a Boca Raton : ▼b CRC Press, ▼c 2022.
300 ▼a xxxiii, 397 p. : ▼b ill. (some col.) ; ▼c 25 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a The R series
504 ▼a Includes bibliographical references (p. 387-394) and index.
505 0 ▼a Introduction to behavior and machine learning -- Predicting behavior with classification models -- Predicting behavior with ensemble learning -- Exploring and visualizing behavioral data -- Preprocessing behavioral data -- Discovering behaviors with unsupervised learning -- Encoding behavioral data -- Predicting behavior with deep learning -- Multi-user validation -- Detecting abnormal behaviors.
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"-- ▼c Provided by publisher.
650 0 ▼a Behavioral assessment ▼x Data processing.
650 0 ▼a Task analysis ▼x Data processing.
650 0 ▼a Machine learning.
650 0 ▼a R (Computer program language).
830 0 ▼a R series.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 155.28 G216b 등록번호 111859171 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

1. Introduction to Behavior and Machine Learning
2. Predicting Behavior with Classification Models
3. Predicting Behavior with Ensemble Learning
4. Exploring and Visualizing Behavioral Data
5. Preprocessing Behavioral Data
6. Discovering Behaviors with Unsupervised Learning
7. Encoding Behavioral Data
8. Predicting Behavior with Deep Learning
9. Multi-User Validation
10. Detecting Abnormal Behaviors
Appendix A. Setup Your Environment
Appendix B. Datasets

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