Learning from your neighbour

Learning from your patients.

INSTANCE-BASED LEARNING: Nearest Neighbour with Generalisation

Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest...

Cutting Your Teeth: Learning from Rare (One or More) Experiences

In this paper, we explore learning-by-doing in a unique and important setting not previously exploredthe context of one or more complex experiences encountered in novel circumstances. We explore characteristics that lead to learning from small sets of prior experiences. We use data from survey responses of 2,111 entrepreneurs to examine performance of startup firms as a measure of outcomes prod...

Round-dance neighbour designs from terraces

In a round-dance neighbour design, an odd number v of objects is arranged successively in (v − 1)=2 rings (circular blocks) such that any two of the objects are adjacent to one another in exactly one ring. A round-dance neighbour design is also a Hamiltonian decomposition of the complete graph on v vertices. We show how such designs can be constructed from terraces, which are building blocks fo...

On neighbour-distinguishing colourings from lists

Let G be a finite simple graph, let C be a set of colours (in this paper we shall always suppose C ⊆ N) and let f : E(G) → C be an edge colouring of G. The colour set of a vertex v ∈ V (G) with respect to f is the set Sf (v) of colours of edges incident to v. The colouring f is neighbour-distinguishing if it distinguishes any two adjacent vertices by their colour sets, i.e., Sf (u) 6= Sf (v) wh...