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Data Science #18 - The k-nearest neighbors algorithm (1951)
In the 18th episode we go over the original k-nearest neighbors algorithm;
Fix, Evelyn; Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties USAF School of Aviation Medicine, Randolph Field, Texas
They introduces a nonparametric method for classifying a new observation 𝑧 z as belonging to one of two distributions, 𝐹 F or 𝐺 G, without assuming specific parametric forms.
Using 𝑘 k-nearest neighbor density estimates, the paper implements a likelihood ratio test for classification and rigorously proves the method's consistency.
The work is a precursor to the modern 𝑘 k-Nearest Neighbors (KNN) algorithm and established nonparametric approaches as viable alternatives to parametric methods.
Its focus on consistency and data-driven learning influenced many modern machine learning techniques, including kernel density estimation and decision trees.
This paper's impact on data science is significant, introducing concepts like neighborhood-based learning and flexible discrimination.
These ideas underpin algorithms widely used today in healthcare, finance, and artificial intelligence, where robust and interpretable models are critical.
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By Mike E
3
33 ratings
In the 18th episode we go over the original k-nearest neighbors algorithm;
Fix, Evelyn; Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties USAF School of Aviation Medicine, Randolph Field, Texas
They introduces a nonparametric method for classifying a new observation 𝑧 z as belonging to one of two distributions, 𝐹 F or 𝐺 G, without assuming specific parametric forms.
Using 𝑘 k-nearest neighbor density estimates, the paper implements a likelihood ratio test for classification and rigorously proves the method's consistency.
The work is a precursor to the modern 𝑘 k-Nearest Neighbors (KNN) algorithm and established nonparametric approaches as viable alternatives to parametric methods.
Its focus on consistency and data-driven learning influenced many modern machine learning techniques, including kernel density estimation and decision trees.
This paper's impact on data science is significant, introducing concepts like neighborhood-based learning and flexible discrimination.
These ideas underpin algorithms widely used today in healthcare, finance, and artificial intelligence, where robust and interpretable models are critical.
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