Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. In the present book, fifteen typical literatures about Machine learning published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on Machine learning. We hope this book can demonstrate advances in Machine learning as well as give references to the researchers, students and other related people.
Components of the Book:
- Chapter1
Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning
- Chapter2
Current Applications and Future Impact of Machine Learning in Radiology
- Chapter3
Involvement of Machine Learning Tools in Healthcare Decision Making
- Chapter4
Machine Learning Photovoltaic String Analyzer
- Chapter5
The Challenges of Machine Learning and Their Economic Implications
- Chapter6
Integrating machine learning and multiscale modeling— perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
- Chapter7
Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning
- Chapter8
The promise of machine learning in predicting treatment outcomes in psychiatry
- Chapter9
A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems
- Chapter10
Exploiting Machine Learning for End-To-End Drug Discovery and Development
- Chapter11
The Role of Machine Learning in the Understanding and Design of Materials
- Chapter12
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening
- Chapter13
Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?
- Chapter14
Machine Learning in Human Olfactory Research
- Chapter15
How Machine Learning will Transform Biomedicine
Readership:
Students, academics, teachers and other people attending or interested in Advances in Machine Learning
Johannes Burdack
Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, Germany
Sven Giesselbach
Knowledge Discovery, Fraunhofer-Institute of Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
Sabrina Daffner
Qimoto, Doctors‘ Surgery for Sport Medicine and Orthopedics, Wiesbaden, Germany
Gamage Upeksha Ganegoda
Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
Sandy Rodrigues
Instituto de Telecomunicacoes of the Instituto Superior Tecnico of the University of Lisbon, 1049-001 Lisbon, Portugal
and more...