Kernelling: meaning, definitions and examples

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kernelling

 

[ หˆkษœrnษ™หŒlษชล‹ ]

Noun
Context #1 | Noun

computer science

Kernelling refers to a technique used in machine learning to enable the learning algorithms to operate in a transformed feature space. By applying kernelling, data points can be mapped into a higher-dimensional space where linear separability is more feasible. This method is particularly effective with algorithms such as Support Vector Machines (SVM), where it enhances the capacity of the algorithm to learn complex patterns. In essence, kernelling provides a way to interpret and analyze data in ways that standard linear models cannot.

Synonyms

dimensional transformation, feature mapping, kernel trick.

Examples of usage

  • The model performance improved significantly after applying kernelling.
  • Kernelling is essential for working with non-linear data.
  • Using kernelling allows for better classification accuracy in SVMs.

Translations

Translations of the word "kernelling" in other languages:

๐Ÿ‡ต๐Ÿ‡น kernelizaรงรฃo

๐Ÿ‡ฎ๐Ÿ‡ณ เค•เคฐเฅเคจเฅ‡เคฒเคฟเค‚เค—

๐Ÿ‡ฉ๐Ÿ‡ช Kernelisierung

๐Ÿ‡ฎ๐Ÿ‡ฉ pembentukan kernel

๐Ÿ‡บ๐Ÿ‡ฆ ะบะตั€ะฝะตะปั–ะฝะณ

๐Ÿ‡ต๐Ÿ‡ฑ kernelizacja

๐Ÿ‡ฏ๐Ÿ‡ต ใ‚ซใƒผใƒใƒชใƒณใ‚ฐ

๐Ÿ‡ซ๐Ÿ‡ท kernelling

๐Ÿ‡ช๐Ÿ‡ธ kernelling

๐Ÿ‡น๐Ÿ‡ท kernelleme

๐Ÿ‡ฐ๐Ÿ‡ท ์ปค๋„๋ง

๐Ÿ‡ธ๐Ÿ‡ฆ ุชูƒุฑูŠุฑ ุงู„ู†ูˆุงุฉ

๐Ÿ‡จ๐Ÿ‡ฟ kernelizace

๐Ÿ‡ธ๐Ÿ‡ฐ kernelizรกcia

๐Ÿ‡จ๐Ÿ‡ณ ๆ ธๅŒ–

๐Ÿ‡ธ๐Ÿ‡ฎ kernelizacija

๐Ÿ‡ฎ๐Ÿ‡ธ kjarnaferli

๐Ÿ‡ฐ๐Ÿ‡ฟ ัะดั€ะพะปั‹า› ำฉาฃะดะตัƒ

๐Ÿ‡ฌ๐Ÿ‡ช แƒ™แƒ”แƒ แƒœแƒ”แƒšแƒ˜แƒœแƒ’แƒ˜

๐Ÿ‡ฆ๐Ÿ‡ฟ kรถrpรผ tษ™yini

๐Ÿ‡ฒ๐Ÿ‡ฝ kernelling

Etymology

The term 'kernelling' has its roots in the field of statistics and machine learning, particularly emerging from developments in support vector machines during the 1990s. It derives from the word 'kernel', which refers to a function that computes the inner product of two vectors in a high-dimensional space without explicitly transforming the data into that space. This concept was popularized by researchers such as Vladimir Vapnik and Alexey Chervonenkis, who explored the implications of kernel methods for statistical learning theory. As computing power increased and the need for more sophisticated algorithms arose, kernelling became a fundamental technique in various applications such as image recognition, bioinformatics, and natural language processing.