scholarly journals A comparative study of the leading machine learning techniques and two new optimization algorithms

2019 ◽  
Vol 272 (3) ◽  
pp. 1041-1057 ◽  
Author(s):  
P. Baumann ◽  
D.S. Hochbaum ◽  
Y.T. Yang
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Optimization Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques

A comparative study of hybrid machine learning techniques for customer lifetime value prediction

Kybernetes ◽  
2013 ◽  
Vol 42 (3) ◽  
pp. 357-370 ◽  
Author(s):  
Chih‐Fong Tsai ◽  
Ya‐Han Hu ◽  
Chia‐Sheng Hung ◽  
Yu‐Feng Hsu
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Hybrid Machine

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

2020 ◽  
pp. 1096-1117
Author(s):  
Rodrigo Ibañez ◽  
Alvaro Soria ◽  
Alfredo Raul Teyseyre ◽  
Luis Berdun ◽  
Marcelo Ricardo Campo
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Gesture Recognition ◽  
Microsoft Kinect ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Natural Interaction ◽  
Computer Communication ◽  
Human Computer Interfaces ◽  
Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

scholarly journals A Comparative Study of Machine Learning Techniques to Simulate Land Use Changes

2018 ◽  
Vol 47 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Mohammad Ahmadlou ◽  
Mahmoud Reza Delavar ◽  
Anahid Basiri ◽  
Mohammad Karimi
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Comparative Study ◽  
Land Use Changes ◽  
Machine Learning Techniques ◽  
Learning Techniques
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