… on the base class test set during incremental learning. (a) Initially, the base class exemplars
… It stores the exemplars and the statistics of historical classes to rectify the prediction scores. …

… Students’ academic performance prediction is one of the most important applications of
Educational … The attainment of student outcomes in an Outcome-based Education (OBE) system …

… incremental learning based on the wavelet neural network (WNN) ensemble, and used negative
correlation learning (… accuracy rate of incremental learning with nonintegration becomes …

… in incremental learning, which can be applied to relate and facilitate different learning …
Hence, we need to align the prediction of old and new model through knowledge distillation …

… for an institute to forecast student performance, classify individuals based on their talents,
and attempt to enhance their performance in future tests. Students should be advised well in …

… incrementally self-update with detected instances. There are two main challenges considering
the classincremental learning… classes from known ones by prediction uncertainty, and use …

… a decision tree based boosting learning algorithm, … prediction results are obtained by
summing the predicting results of all the weak classifiers. Given a training sample x, the prediction …

… SGD is a gradient-based optimization technique that updates the model parameters
incrementally, rather than on the entire training set at once (which is what normal gradient descent …

… The purpose of this study was to assess the system’s ability to predict student outcome based
on … algorithm in both its batch and incremental variant. The purpose of the analysis was to …

The problem of predicting students’ performance has been recently tackled by using matrix …
based recommender systems. This problem consists of predicting the unknown performance …