Incremental Similarity for real-time on-line incremental learning systems

• New Incremental Similarity for incremental learning problems.
• Handling the processing time vs. accuracy issue, the learning on the fly as well as the lack of data at the beginning of the learning.
• Incorporation of Incremental Similarity into various classification models.
• Extensive comparison and evaluation of various models using our incremental learning framework.

The expectation of higher accuracy in recognition systems brings the problem of higher complexity. In this paper we introduce a novel Incremental Similarity (IS) that maintains high accuracy while preserving low complexity. We apply IS to on-line and incremental learning tasks, where the need of low complexity is of significant need. Using IS enables the system to directly compute with the samples themselves and update only few parameters in an incremental manner. We empirically prove its efficiency on several evolving models and show that by using IS they achieve competitive results and outperform the baseline models. We also consider the problem of incremental learning used to handle fast growing datasets. We present a very detailed comparison for not only evolving models, but also for the well-known batch models, showing the robustness of our proposal. We perform the evaluation on various classification problems to show the wide application of evolving models and our proposed IS.