Dynamic Shape Learning and Forgetting

In this paper, we present a system capable of dynamically learning shapes in a way that also allows for the dynamic deletion of shapes already learned. It uses a self-balancing Binary Search Tree (BST) data structure in which we can insert shapes that we can later retrieve and also delete inserted shapes. The information concerning the inserted shapes is distributed on the tree’s nodes in such a way that it is retained even after the structure of the tree changes due to insertions, deletions and rebalances these two operations can cause. Experiments show that the structure is robust enough to provide similar retrieval rates after many insertions and deletions.

• Type: Journal
• Year: 2010
• Where: ICANN
• Authors: Nikolaos Tsapanos, Anastasios Tefas, Ioannis Pitas
• Keywords: Dynamic Deletion, ICANN 2010, Inserted Shapes, Neural Networks, Self-balancing Binary Search Tree