I came to know about Vapnik–Chervonenkis during my study on ML. All I get it to represent the power of a classifier. But I don't understand how to calculate it exactly.
N set of data points, by using a linear classifier to classify this data points. What is the
VC dimension for this classifier? From My understanding, it may be
2^N! But not sure.
I was actually reading a blog post on this topic and they showed some co-relation with the posterior distribution. What is the posterior distribution for a given set of data point N that if a model has the following prior,
x1,x2,…,xn∼N(μ,σ20) and μ∼N(m0,s0)
σ20 are known.
Is this a,
- Beta Distribution or
- Normal Distribution or
- Gamma Distribution or
- Inverse Gamma Distribution
Any help will be highly appreciated.