Kernelling: meaning, definitions and examples
๐ป
kernelling
[ หkษrnษหlษชล ]
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:
๐ต๐น kernelizaรงรฃo
๐ฎ๐ณ เคเคฐเฅเคจเฅเคฒเคฟเคเค
๐ฉ๐ช Kernelisierung
๐ฎ๐ฉ pembentukan kernel
๐บ๐ฆ ะบะตัะฝะตะปัะฝะณ
๐ต๐ฑ kernelizacja
๐ฏ๐ต ใซใผใใชใณใฐ
๐ซ๐ท kernelling
๐ช๐ธ kernelling
๐น๐ท kernelleme
๐ฐ๐ท ์ปค๋๋ง
๐ธ๐ฆ ุชูุฑูุฑ ุงูููุงุฉ
๐จ๐ฟ kernelizace
๐ธ๐ฐ kernelizรกcia
๐จ๐ณ ๆ ธๅ
๐ธ๐ฎ kernelizacija
๐ฎ๐ธ kjarnaferli
๐ฐ๐ฟ ัะดัะพะปัา ำฉาฃะดะตั
๐ฌ๐ช แแแ แแแแแแแ
๐ฆ๐ฟ kรถrpรผ tษyini
๐ฒ๐ฝ 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.