Decoding of finger movement using kinematic model classification and regression model switching

2016 ◽  
Author(s):  
Ayman Elgharabawy ◽  
Manal Abdel Wahed
Keyword(s):  
Regression Model ◽  
Kinematic Model ◽  
Finger Movement ◽  
Model Classification ◽  
Classification And Regression

The sparse signomial classification and regression model

2012 ◽  
Vol 216 (1) ◽  
pp. 257-286 ◽  
Author(s):  
Kyungsik Lee ◽  
Norman Kim ◽  
Myong K. Jeong
Keyword(s):  
Regression Model ◽  
Classification And Regression

A spatial, climate-determined risk rating for Scleroderris disease of pines in Ontario

1998 ◽  
Vol 28 (9) ◽  
pp. 1398-1404 ◽  
Author(s):  
L A Venier ◽  
A A Hopkin ◽  
D W McKenney ◽  
Y. Wang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Classification Accuracy ◽  
Logistic Regression Model ◽  
Distribution Data ◽  
Parameter Estimates ◽  
Final Model ◽  
Probability Of Occurrence ◽  
Model Classification ◽  
Historical Distribution

We used historical distribution data of Scleroderris disease (caused by the fungus Gremmeniella abietina var. abietina (Lagerb.) Morelet) in Ontario to model its probability of occurrence as a function of climate factors. A logistic regression model of the probability of occurrence as a function of the mean temperature of the coldest quarter and the precipitation of the coldest quarter was a very good fit. The concordance (index of classification accuracy) of the model was 84%. We subsampled the data repeatedly, generated new parameter estimates, and tested the predictions against data not included in the model. Classification accuracy was similar for each subsample model; therefore, we concluded that the final model is stable. Gridded estimates of the climate variables were used to spatially extend the two-variable logistic regression model and produce a probability of occurrence map for Scleroderris disease across Ontario. The predicted map of probability of occurrence fits well with the map of the observed locations of the disease. These results lend credence to previous work that suggests that distribution of Scleroderris disease is strongly influenced by climate. The classification results also suggest that this model is a useful tool for assessing the risk of Scleroderris disease throughout Ontario.

scholarly journals Measuring Trust in Business Relations between Tourist Facilities on One Thematic Touristic Route

2019 ◽  
Vol 11 (14) ◽  
pp. 3935 ◽  
Author(s):  
Adam R. Szromek ◽  
Mateusz Naramski
Keyword(s):  
Regression Model ◽  
Statistical Methods ◽  
Linear Correlation ◽  
Organizational Trust ◽  
Multiple Regression Model ◽  
Trust Level ◽  
Strong Linear Correlation ◽  
Communication Efficiency ◽  
Organizational Relations ◽  
Classification And Regression

The aim of this article was to identify features and attributes of tourist facilities that affect trust among them and allows estimating the level of trust among any given site on a touristic route. The level of trust can be a key feature that affects the capability of tourist facilities to create and enter complex relations. It is also crucial for planning their future and sustainability. Therefore, measuring trust between tourist facilities plays a major role in the management of inter-organizational relations. The authors used statistical methods in order to identify features that influence the level of inter-organizational trust between these kinds of facilities. The analyzed data comes from research that was conducted in 2017 and describes 42 tourist facilities that operate within the Industrial Monuments Route (IMR)—a Polish route in the region of Silesia. A strong linear correlation was found between variables that described efficiency of communication and trust level. The Classification and Regression Trees (C&RTs) method was used to identify features of tourist facilities that determine the efficiency of communication among them. Afterwards, the obtained data was used to create a multiple regression model that allowed estimating the level of trust between any given site on the route, identifying at the same time that features like communication efficiency, proximity and some institutional similarities have the greatest impact among tourist sites with regard to inter-organizational trust.

scholarly journals Facial Age Estimation Method Based on Fusion Classification and Regression Model

2018 ◽  
Vol 232 ◽  
pp. 02021
Author(s):  
Fengbing Jiang ◽  
Yu Zhang ◽  
GuoLiang Yang
Keyword(s):  
Regression Model ◽  
Age Estimation ◽  
Age Distribution ◽  
Estimation Method ◽  
Classification Model ◽  
Large Individual ◽  
Face Images ◽  
Classification And Regression ◽  
Better Than

Due to the large individual differences in the facial features of each person and the fact that the age has a certain time sequence, the age estimation based on face images faces certain difficulties. This article proposes a method based on fusion classification and regression model: A classification model and a regression model are added to the convolutional neural network to train the network under the premise of sharing convolutional layer parameters. The classification of the age of the label is used to code the age distribution, and the age is regressed using the Euclidean distance. The final predicted value of the model is the average of the two. Experiments show that the effect of fusion classification and regression model is better than that of a single model, which improves the accuracy of age estimation.

Applications of Classification and Regression Tree Methods in Roadway Safety Studies

1996 ◽  
Vol 1542 (1) ◽  
pp. 1-5 ◽  
Author(s):  
J. Richard Stewart
Keyword(s):  
Regression Model ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Response Variable ◽  
Continuous Response ◽  
Complex Interactions ◽  
Roadway Safety ◽  
Safety Studies ◽  
Tree Methods ◽  
Classification And Regression

In recent years a number of nonparametric regression-type statistical procedures have been developed. Classification and regression trees (CART) is one such method that can be used as a classifier for a discrete-valued response variable or as a regression model for a continuous response variable. Advantages of CART over many other methods are its ability to include a relatively large number of independent variables and to identify complex interactions among these variables. A brief description of the CART procedure is given, and its application as a classifier and as a regression model to highway safety analyses is illustrated.

scholarly journals Freeway Incident Frequency Analysis Based on CART Method

2014 ◽  
Vol 26 (3) ◽  
pp. 191-199 ◽  
Author(s):  
Xuecai Xu ◽  
Željko Šarić ◽  
Ahmad Kouhpanejade
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Negative Binomial ◽  
Classification And Regression Tree ◽  
Future Research ◽  
Negative Binomial Regression Model ◽  
Classification And Regression ◽  
The Impact ◽  
The Relationship ◽  
Incident Frequency

Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.

scholarly journals Predicting Student Performance using Classification and Regression Trees Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3349-3356 ◽  
Keyword(s):  
At Risk ◽  
Student Performance ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Course Performance ◽  
Model Classification ◽  
Study Materials ◽  
Classification And Regression ◽  
Predicting Student Performance ◽  
The Impact

Now a days Internet and Web technologies providing students opportunities for flexible interactivity with study materials, peers and instructors. And also generating large amounts of usage data that can be processed and reveal behavioral patterns of study and learning. In this paper, to predict course performance we extracted data from a Moodlebased blended learning course and build a student model. Classification and Regression Trees (CART) decision tree algorithm was used to classify students and predict those at risk, based on the impact of four online activities: message exchanging, group wiki content creation, course files opening and online quiz taking. The correct classifications in results prove that the model is sensitive to categorize very specific groups at risk.

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