Nearest neighbours reveal fast and slow components of motor learning

Smoothing Using Nearest Neighbours

Feature Reduction and Nearest Neighbours

Feature reduction is a major preprocessing step in the analysis of highdimensional data, particularly from biomolecular high-throughput technologies. Reduction techniques are expected to preserve the relevant characteristics of the data, such as neighbourhood relations. We investigate the neighbourhood preservation properties of feature reduction empirically and theoretically. Our results indic...

Applications of Nearest Neighbours Statistics

Jorg D. Wichard, Ulrich Parlitz and Werner Lauterborn Drittes Physikalisches Institut, Georg-August-Universitat Gottingen, D-37073 Gottingen, Germany [email protected] Abstract| Based on an e cient method for nding nearest neighbours in the phase space of a dynamical system, several applications of nearest neighbour statistics are presented, including methods to detect nonstationarity...

Learning Fast and Slow

We propose two distinct levels of learning for general autonomous intelligent agents. Level 1 consists of fixed architectural learning mechanisms that are innate and automatic. Level 2 consists of deliberate learning strategies that are controlled by the agent’s knowledge. We describe these levels and provide an example of their use in a task-learning agent. We also explore other potential leve...

Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours

Many pattern recognition tasks make use of the k nearest neighbour (k–NN) technique. In this paper we are interested on fast k– NN search algorithms that can work in any metric space i.e. they are not restricted to Euclidean–like distance functions. Only symmetric and triangle inequality properties are required for the distance. A large set of such fast k–NN search algorithms have been develope...