R Ade, PR Deshmukh - Proceedings of 3rd International …, 2014 - ieeexplore.ieee.org
The amount of students data in the education system databases is growing day by day, so the knowledge taken out from these data need to be updated continuously. The training set …
QL Zhao, YH Jiang, M Xu - 2011 Eighth International …, 2011 - ieeexplore.ieee.org
Bagging, a widely used ensemble method, is simple and fast, and can generate heterogeneous base classifiers. This research proposes an incremental learning algorithm …
The ability to predict the career of students can be beneficial in a huge number of different techniques which are connected with the education structure. Student's marks in …
P Kulkarni, R Ade - International Journal of Computer Applications, 2014 - Citeseer
It is necessary to use Student dataset in order to analyze student's performance for future improvements in study methods and overall curricular. Incremental learning methods are …
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level distance learning. Students' marks in a few written …
M Han, M Tong, M Chen, J Liu… - 2017 6th IIAI International …, 2017 - ieeexplore.ieee.org
With data accumulating rapidly in education, it is possible for researchers to predict students academic performance. Among the prediction models, the machine learning model is …
X Yang, B Yuan, W Liu - 2009 Chinese Conference on Pattern …, 2009 - ieeexplore.ieee.org
This paper investigates an interesting question of solving incremental learning problems using ensemble algorithms. The motivation is to help classifiers learn additional information …
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental …
R Polikar, S Krause, L Burd - Proceedings of the International …, 2003 - ieeexplore.ieee.org
An incremental learning algorithm based on weighted majority voting of an ensemble of classifiers is introduced for supervised neural networks, where the voting weights are …