Sign up to save your podcasts
Or
Share Data Science #18 - The k-nearest neighbors algorithm (1951)
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
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.
View all episodes
By Mike E
3.8
55 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.
More shows like Data Science Decoded
View all
Freakonomics Radio
32,003 Listeners
The Joe Rogan Experience
229,169 Listeners
StarTalk Radio
14,322 Listeners
More or Less
890 Listeners
Talk Python To Me
585 Listeners
The Quanta Podcast
532 Listeners
Science Friday
6,401 Listeners
Super Data Science: ML & AI Podcast with Jon Krohn
302 Listeners
Pod Save America
87,348 Listeners
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
4,182 Listeners
Practical AI
211 Listeners
Machine Learning Street Talk (MLST)
95 Listeners
Hard Fork
5,512 Listeners
The Rest Is History
15,272 Listeners
The Astrophysics Podcast
54 Listeners