Скачать с ютуб Kernel Function in Support Vector Machine SVM || Lesson 83 || Machine Learning || Learning Monkey || в хорошем качестве

Kernel Function in Support Vector Machine SVM || Lesson 83 || Machine Learning || Learning Monkey ||

#machinelearning#learningmonkey In this class, we discuss Kernel Function in Support Vector Machine SVM. We overcome that cost using Kernel Function in Support Vector Machine SVM Previously we discussed the use of transforming data. The difficulty in transformation is computationally cost. Let's take transformed features. We do dot products of those transformed features. These can be written in the form of a.b square. Where a.b is the dot product of features before the transformation. Now if we use this a.b squared value instead of a.b. in the support vector machine solution. We are identifying vectors in the transformed feature space. That is the reason why the kernel function identifying support vectors in transformed space without doing computation. The function that can write transformed feature vectors in the form of vectors before transformed those we call it as kernel functions. we can do the transformation in many ways. But all the transformation functions are not kernel functions. The one that satisfies the above-discussed property is the kernel function. There are so many domain-specific kernel functions. Most used kernel functions are polynomial function and RBF function. RBF stands for Radio Base Frequency. Link for playlists:    / @learningmonkey   Link for our website: https://learningmonkey.in Follow us on Facebook @   / learningmonkey   Follow us on Instagram @   / learningmonkey1   Follow us on Twitter @   / _learningmonkey   Mail us @ [email protected]