This paper is intended to introduce a closer look at incremental learning by developing the two concepts of informationally incremental learning and operationally incremental learning. These concept are applied to the problem of learning containment decision lists for demonstrating its relevance.

Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base with only a few labeled training data from classes. Most related prior works are on incremental that rely availability abundant samples per class substantially limits scalability to real-world setting where can be scarce. In this paper, we propose Incremental-DETR does via fine-tuning ...

This paper discusses work in progress and introduces a partial memory incremental learning methodology. The incremental learning architecture uses hypotheses induced from training examples to determine representative examples, which are maintained for future learning. Criticism and reinforcement from the environment or the user invoke incremental learning once the system is deployed. Such an ar...

One of incremental learning-based neural networks that theoretically guarantees the optimal generalization capability and provides exactly the same generalization capability as that obtained by batch learning is incremental projection generalizing neural networks. This paper will describe a two-level learning hierarchy for constructing the networks. An incremental projection learning in neural ...

The present study aims at insights into the nature of incremental learning in the context of Gold’s model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way differe...

The present study aims at insights into the nature of incremental learning in the context of Gold’s model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way differe...

The present study aims at insights into the nature of incremental learning in the context of Gold’s model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way differe...