000			03840cam a22004098i 4500
001			22264078
003			IN-BdCUP
005			20240516142155.0
008			210928s2022 enk d 001 0 eng
010			_a 2021028230
020			_a9781032067049
040			_aLBSOR/DLC _beng _erda _cDLC
041			_aeng
042			_apcc
082	0	0	_a155.28 _bCEJ
100	1		_aCeja, Enrique Garcia _eAuthor.
245	1	0	_aBehavior analysis with machine learning using R / _cEnrique Garcia Ceja.
250			_a1st Edition.
263			_a2112
264		1	_aLondon ; _aBoca Raton : _bCRC Press, _c2022.
300			_axxxiii, 397 p.; _c22 cm _fHB
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490			_aChapman & Hall/CRC The R Series.
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction 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"-- _cProvided by publisher.
650		0	_aBehavioral assessment _xData processing.
650		0	_aTask analysis _xData processing.
650		0	_aMachine learning.
650		0	_aR (Computer program language)
776	0	8	_iOnline version: _aGarcia Ceja, Enrique. _tBehavior analysis with machine learning using R. _bFirst edition _dLondon ; Boca Raton : CRC Press, 2022 _z9781003203469 _w(DLC) 2021028231
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c52604 _d52604