# Catalogue of Artificial Intelligence Techniques

## Instance-Based Learning

**Aliases:**
Locally Weighted Linear Regression, k-Nearest Neighbour

**Keywords:**
classification, instance, learning, nearest neighbour, patter, recognition

### Categories: Pattern Recognition and Image Processing

Author(s): **Alastair McFarlane**

Instance-based learning is a term used to describe several methods , the simplest of which is mentioned below, used for the classification of data in patter recognition problems.

The way a new data point is classified depends on previous data which has been examined: the training set, for example.

The simplest instance-based approach is called the k-nearest neighbour algorithm. This algorithm plots data points in n-dimensional space (R n). The easiest example of this is 2-dimensional space, where all data points are plotted on the plane.

When a new data point is encountered, it is placed in the same class as it's “nearest neighbour”, measured as the actual distance between the two points in n-dimensional space (the “Euclidean Distance”)

### References:

- Mitchell, Tom M.,
*Machine Learning*, Ch 8. Instance-Based Learning, McGraw-Hill, 1997, pp.230--231.

### Comments:

No comments.