What Is K Nearest Neighbor Algorithm
The k-nearest neighbors technique, also known as k-NN, is a non-parametric supervised learning method that was initially created in 1951 by Evelyn Fix and Joseph Hodges in the field of statistics. Thomas Cover later expanded on the original concept. It has applications in both regression and classification. In both scenarios, the input is made up of the k training instances in a data collection that are the closest to one another. Whether or not k-NN was used for classification or regression, the results are as follows:The output of a k-nearest neighbor classification is a class membership. A plurality of an item's neighbors votes on how the object should be classified, and the object is then assigned to the class that is most popular among its k nearest neighbors (where k is a positive number that is often quite small). If k is equal to one, then the object is simply classified as belonging to the category of its single closest neighbor.The result of a k-NN regression is the value of a certain property associated with an object. This value is the average of the values of the k neighbors that are the closest to the current location. If k is equal to one, then the value of the output is simply taken from the value of the one nearest neighbor.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: K-nearest neighbors algorithm
Chapter 2: Supervised learning
Chapter 3: Pattern recognition
Chapter 4: Curse of dimensionality
Chapter 5: Nearest neighbor search
Chapter 6: Cluster analysis
Chapter 7: Kernel method
Chapter 8: Large margin nearest neighbor
Chapter 9: Structured kNN
Chapter 10: Weak supervision
(II) Answering the public top questions about k nearest neighbor algorithm.
(III) Real world examples for the usage of k nearest neighbor algorithm in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of k nearest neighbor algorithm' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of k nearest neighbor algorithm.
