In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

the purpose of this research is to investigate the effects of intelligence beliefs and achievement motivation on learning strategies and academic performance of agricultural students. this research was descriptive - correlation with data collected using questionnaire. the statistical population included students in colleges of agriculture at universities of tehran, bouali sina, ilam, shahid bah...

Historically, inductive machine learning has focused on nonincremental learning tasks, i.e., where the training set can be constructed a priori and learning stops once this set has been duly processed. There are, however, a number of areas, such as agents, where learning tasks are incremental. This paper defines the notion of incrementality for learning tasks and algorithms. It then provides so...

The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot’s programmer cannot predict all possible external failures which robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle originating from change in environment. Existing typically offers special-purpose solutions. Furthe...

SVM-based incremental learning, which can make a user-relevant recognition system quickly adapt to specific users' preference without losing its general performance, is an elegant solution for user adaptation problems in on-line graphics recognition system. Two learning strategies (repetitive learning and incremental learning), two incremental learning algorithms (Syed et al.'s and Xiao et al.'...

Deep learning research over the past years has shown that by increasing the scope or difficulty of the learning problem over time, increasingly complex learning problems can be addressed. We study incremental learning in the context of sequence learning, using generative RNNs in the form of multi-layer recurrent Mixture Density Networks. While the potential of incremental or curriculum learning...