Machine Learning | The Vapnik-Chervonenkis Dimension

In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the capacity (complexity, expressive power, richness, or flexibility) of a space of functions that can be learned by a statistical classification algorithm. #MachineLearning #VCDimension

[purplemelodies]

At around 10:12 you told we cannt classify A B C D using circle... but we can put point C inside circle and remaining points outside the circle.

[sgf]

Thanks. Really good video. I had difficulty understanding this for years. Your 5 minutes helped immensely

[SAN-terp]

Sir please make a video on probability approximate correct(PAC)🙏

[crazytech]

Thanks for the video sir for giving best explanation 👍

[RahulSingh-upjo]

10:27 you say that the VC dimension of the 2 dimensional space is 3 but at the end 11:22 you gave an example saying three colinear points can not be shattered. So how is the VC dimension 3?

[oscura]

Thank you so much! In the first 4 min of your video i understood more than in my lecture class.

[Marimenezesg]

I was reading the Vapnik article and I just couldn't understand this concept. I wish I came to your video sooner. Great explanation!

[bdurgaramprasad]

Sir, Can't we just draw a circle for the negative point in case of the collinear example you have provided??

[baderal-hamdan]

In the last case which was colinear what will be the VC dimension in this case?

[palavracomentada]

Hello Ranji, I'm Studing The Vapnik-Chervonenkis Dimension like Complex Mesure, have you some video about it?

[sibinsam]

The max no of points that can be shattered by a function which you define

[teegnas]

Hello Rajni, please try and make videos on multidimensional scaling & Sammon's mapping. The ones you have covered are linear dimensionality reduction techniques, but it would be great if you make videos on some of the non-linear techniques.