Modern deep learning methods have achieved great success in machine and computer vision fields by a set of pre-defined datasets. Howerver, these perform unsatisfactorily when applied into real-world situations. The reason this phenomenon is that new tasks leads the trained model quickly forget knowledge old tasks, which referred to as catastrophic forgetting. Current state-of-the-art incrementa...

1 Background and motivation incremental learning (e.g., in decision tree design the entire tree algorithm must be re-run) while symbolic The problem of induction or "learning from exalgorithms often incorporate incremental learning as amples" can roughly be divided into two distinct cata basic mechanism. On the other hand, symbolic egories, namely the symbolic manipulation approach techniques c...

We consider the problem of incremental transfer of behaviors in a multi-agent learning test bed (keep-away soccer) consisting of homogeneous agents (keepers). One method for this incremental transfer is called the easy missions approach, and seeks to synthesize solutions for complex tasks from those for simpler ones. In genetic programming (GP), this has been achieved by identifying goals and f...

While the areas of applications in data mining are growing substantially, it has become extremely necessary for incremental learning methods to move a step ahead. The tremendous growth of unlabeled data has made incremental learning take up a big leap. Starting from BI applications to image classifications, from analysis to predictions, every domain needs to learn and update. Incremental learni...

We present an evaluation of incremental learning algorithms for the estimate of HMM parameters. The main goal was to investigate incremental learning algorithms that can replace traditional batch learning techniques, incorporating the advantages of incremental techniques for designing complex pattern recognition systems. Experiments were carried out on isolated digits, extracted from the NIST S...

Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability ...

Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component which cannot be achieved by straightforward usage of off-the-shelf machine learning methods (such as Gaussian process regression or support vector regression). In this paper,...