Machine Learning

k-Nearest Neighbor Learning and  Locally Weighted Regression  Instance-Based Learning Topics Introduction k -Nearest Neighbor Learning Locally Weighted Regression Radial Basis Functions Case-Based Reasoning Remarks on Lazy and Eager Learning. What is Instance-based Learning? Instance-based learning is a type of machine learning that uses a lazy learning approach, where the algorithm stores the training examples and makes predictions based on similarity measures between the new instance and the stored instances.  Introduction Instance-Based Learning is a type of machine learning where the algorithm is given a dataset, and it learns by memorizing the instances in the dataset. The algorithm uses these instances to make predictions on new, unseen instances. The key idea behind instance-based learning is that the function approximator only makes predictions based on the similarity between the new instance and the instances in the dataset.