Self-adjusting binary search trees
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
List organizing strategies using stochastic move-to-front and stochastic move-to-rear operations
SIAM Journal on Computing
Deterministic optimal and expedient move-to-rear list organizing strategies
Theoretical Computer Science
An optimal absorbing list organization strategy with constant memory requirements
Theoretical Computer Science
Self-Organizing Binary Search Trees
Journal of the ACM (JACM)
Self-Organizing Maps
Adaptive Structuring of Binary Search Trees Using Conditional Rotations
IEEE Transactions on Knowledge and Data Engineering
A Relationship Between Self-Organizing Lists and Binary Search Trees
ICCI '91 Proceedings of the International Conference on Computing and Information: Advances in Computing and Information
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established fields of Neural Networks (NNs) and Adaptive Data Structures (ADS) respectively. The field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. This structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data.