… were collected from the student’s management system, to predict the performance at the end
of … techniques for predicting student performance (Hijazi and Naqvi, 2006). 2 Related recent …

… a new memory-based incremental learning strategy to predict students’ performance that
is based on the use of the Genetic Algorithm (GA) to build the training set. The adoption of a …

… dealing with the performance variability of … incremental learning approach to predict the
runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions…

… related to text classification in increment learning. Our proposed model, Learn#, uses the
novel incremental learning to … learning strategy to select the correct prediction results of the …

… For the existing incremental learning techniques, the incorrect prediction of the new class
is always biased towards the old class. This is defined as the bias problem. This problem is …

… predictions, where σ indicates the softmax function. After training on task t, we evaluate the
performance … with previous classes to improve performance based on the assumption that …

… incremental learning algorithms and analyze them according to these properties, (2) introduce
a unified formalization of the class-incremental learning … number of incremental states, (4) …

… Taboola’s incremental learning framework for CTR prediction. Instead of training each
model … During training, we also employ a teacher-student paradigm, where the teacher (trained …

… To help the student model also learn the “reason” for the predictions produced by the
teacher model, we propose a novel EBKD loss for ASR incremental learning to train the student …

… incremental learning, which utilizes both the rehearsal strategy and the distillation strategy.
Let us first formulate class incremental learning. … can effectively alleviate the prediction bias. …