VTU Module - 5 | Instance-Base Learning

18CS71 | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | Module-5 VTU Notes

Instance-Based Learning (IBL) is a machine learning paradigm that focuses on making predictions or decisions based on specific instances or examples in the training data, rather than relying on a pre-defined model. This approach contrasts with traditional model-based learning, as it directly uses the available instances to make predictions for new, unseen data. In this summary, we will explore key concepts within Instance-Based Learning, including k-Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Function, and Case-Based Reasoning.

Introduction to Instance-Based Learning:

Instance-Based Learning, also known as memory-based learning or lazy learning, involves storing and utilizing specific instances from the training dataset to make predictions or decisions. Instead of constructing an explicit model during the training phase, IBL methods directly compare new instances to those in the training set to make predictions.

k-Nearest Neighbour Learning (k-NN):

k-Nearest Neighbour Learning is a fundamental technique in IBL, where predictions are made by considering the majority class or average of the k-nearest neighbors in the feature space. The choice of 'k' determines the number of neighbors influencing the prediction, and it can be adjusted based on the specific characteristics of the dataset.

Locally Weighted Regression:

Locally Weighted Regression is an IBL approach that assigns different weights to different instances based on their proximity to the query instance. Instances closer to the query point have a higher influence on the prediction, while those farther away contribute less. This adaptive weighting mechanism allows the algorithm to focus on the most relevant instances for each prediction.

Radial Basis Function (RBF):

The Radial Basis Function is a kernel method commonly used in IBL. It transforms the feature space by mapping it to a higher-dimensional space using radial basis functions. This transformation can help capture complex relationships between instances, making it particularly useful for non-linear patterns in the data.

Case-Based Reasoning:

Case-Based Reasoning is an IBL paradigm where the system solves new problems by retrieving and adapting solutions from similar past cases. It involves storing a database of cases, each comprising a problem and its corresponding solution. When faced with a new problem, the system retrieves the most relevant cases and adapts their solutions to generate a response.

In summary, Instance-Based Learning is a versatile approach that leverages specific instances from the training set to make predictions or decisions. The discussed techniques such as k-Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Function, and Case-Based Reasoning showcase the diverse methods within IBL, each offering unique advantages depending on the nature of the data and the problem at hand.