Kernel Function is a method used to take data as input and transform into the required form of processing data. “Kernel” is used due to set of mathematical functions used in Support Vector Machine provides the window to manipulate the data. So, Kernel Function generally transforms the training set of data so that a non-linear decision surface is able to transformed to a linear equation in a higher number of dimension spaces. Basically, It returns the inner product between two points in a standard feature dimension.
Standard Kernel Function Equation :
Major Kernel Functions :-
For Implementing Kernel Functions, first of all we have to install “scikit-learn” library using command prompt terminal:
pip install scikit-learn
- Gaussian Kernel: It is used to perform transformation, when there is no prior knowledge about data.
- Gaussian Kernel Radial Basis Function (RBF) : Same as above kernel function, adding radial basis method to improve the transformation.
- Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of neural network, which is used as activation function for artificial neurons.
- Polynomial Kernel: It represents the similarity of vectors in training set of data in a feature space over polynomials of the original variables used in kernel.
- Linear Kernel: used when data is linearly separable.
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- Introduction to Support Vector Machines (SVM)
- Creating linear kernel SVM in Python
- Train a Support Vector Machine to recognize facial features in C++
- Differentiate between Support Vector Machine and Logistic Regression
- Classifying data using Support Vector Machines(SVMs) in Python
- Classifying data using Support Vector Machines(SVMs) in R
- Radial Basis Function Kernel - Machine Learning
- copyreg — Register pickle support functions
- ML | Using SVM to perform classification on a non-linear dataset
- ML | Non-Linear SVM
- SVM Hyperparameter Tuning using GridSearchCV | ML
- Azure Virtual Machine for Machine Learning
- Python | os.major() method
- SymPy | Permutation.support() in Python
- Python PIL | Kernel() method
- ML | Introduction to Kernel PCA
- Mathematical Functions in Python | Set 1 (Numeric Functions)
- Mathematical Functions in Python | Set 2 (Logarithmic and Power Functions)
- Mathematical Functions in Python | Set 3 (Trigonometric and Angular Functions)
- Mathematical Functions in Python | Set 4 (Special Functions and Constants)
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