eksemplarbased

Eksemplarbased, often written as exemplar-based, refers to a class of learning algorithms in machine learning and artificial intelligence that rely on storing and comparing individual training examples, known as exemplars, to make predictions or decisions. Instead of learning a generalized model or a set of rules that summarizes the data, these methods retain the raw training data points. When a new, unseen data point is presented, the algorithm compares it to the stored exemplars to determine its class, value, or behavior.

The core principle behind exemplar-based learning is similarity. The assumption is that data points that are similar to each other in their features will likely share the same label or outcome. Therefore, the prediction for a new instance is typically based on the properties of its nearest neighbors or most similar exemplars in the training set. This approach can be intuitive and requires minimal assumptions about the underlying data distribution.

A prominent example of an exemplar-based algorithm is the k-Nearest Neighbors (k-NN) algorithm. In k-NN, a new data point is classified based on the majority class of its k nearest neighbors in the training data. Other exemplar-based techniques might involve weighted voting based on similarity or more complex similarity measures.

While exemplar-based methods can be effective, especially when the decision boundary is complex or irregular, they can also be computationally expensive, particularly with large datasets, as the entire training set needs to be stored and searched for each prediction. The choice of similarity metric also plays a crucial role in the performance of these algorithms.