A monitoring system to prepare machine learning data sets for earthquake prediction based on seismic-acoustic signals

2015 ◽  
Author(s):  
Alper Vahaplar ◽  
Baris Tekin Tezel ◽  
Resmiye Nasiboglu ◽  
Efendi Nasibov
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Earthquake Prediction ◽  
Acoustic Signals ◽  
Data Sets ◽  
Learning Data

Bayesian Modelling for Machine Learning

2005 ◽  
pp. 236-242
Author(s):  
Paul Rippon ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Bayesian Learning ◽  
Data Sets ◽  
Complex Data ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Complex Data Sets ◽  
Complex Phenomena ◽  
Learning Data

Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating parameters of a process model based on data, modelling and analysis of complex phenomena using multiple sources of information, posterior probabilistic expectation, and so on. In all of these guises, it has exploded in popularity over recent years.

Bayesian Modelling for Machine Learning

2011 ◽  
pp. 421-429
Author(s):  
Paul Rippon ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Bayesian Learning ◽  
Data Sets ◽  
Complex Data ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Complex Data Sets ◽  
Complex Phenomena ◽  
Learning Data

Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating parameters of a process model based on data, modelling and analysis of complex phenomena using multiple sources of information, posterior probabilistic expectation, and so on. In all of these guises, it has exploded in popularity over recent years.

A TOPSIS Data Mining Demonstration and Application to Credit Scoring

2008 ◽  
pp. 1877-1887
Author(s):  
Desheng Wu ◽  
David L. Olson
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Scoring ◽  
Multiple Criteria Decision Analysis ◽  
Data Sets ◽  
Ideal Solution ◽  
Explanatory Variables ◽  
Large Sets ◽  
Order Preference ◽  
Learning Data

The technique for order preference by similarity to ideal solution (TOPSIS) is a technique that can consider any number of measures, seeking to identify solutions close to an ideal and far from a nadir solution. TOPSIS has traditionally been applied in multiple criteria decision analysis. In this paper we propose an approach to develop a TOPSIS classifier. We demonstrate its use in credit scoring, providing a way to deal with large sets of data using machine learning. Data sets often contain many potential explanatory variables, some preferably minimized, some preferably maximized. Results are favorable by a comparison with traditional data mining techniques of decision trees. Proposed models are validated using Mont Carlo simulation.

Identifying Handwork with Machine Learning Data Sets from Sensors Built into Gloves

2021 ◽  
Vol 141 (8) ◽  
pp. 284-291
Author(s):  
Ryohei Matsui ◽  
Iwao Tanuma ◽  
Ryotaro Kawahara ◽  
Naoko Ushio ◽  
Hiroyuki Yoshimoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Sets ◽  
Learning Data

scholarly journals Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

2021 ◽  
Vol 34 (2) ◽  
pp. 541-549 ◽  
Author(s):  
Leihong Wu ◽  
Ruili Huang ◽  
Igor V. Tetko ◽  
Zhonghua Xia ◽  
Joshua Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Sets ◽  
Predictive Toxicology ◽  
Trade Off
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