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About 02-knn Notes
This document contains lecture notes on nearest neighbor methods for machine learning. It introduces the k-nearest neighbors algorithm, which classifies or regresses new examples based on their similarity to the k closest training examples. The key concepts are that k-NN is a lazy learning algorithm that stores all training examples and approximates the target function locally for each prediction based on its nearest neighbors. The document discusses how k-NN relates to categories like eager vs lazy learning, parametric vs nonparametric models, and discriminative vs generative models. It also outlines the k-NN algorithm and various techniques for improving its performance and computational efficiency.
Detailed Information
| Author: | ['Jason Bourne'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 23 |
| Language: | English |
| Format: | |
| Price: | FREE |
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