285 reputation
2311
bio website
location
age
visits member for 2 years, 5 months
seen Aug 4 at 11:52

Jun
12
comment Calculating statistic for multiple runs
absolutely true, thanks for your explanation.
Jun
12
comment Calculating statistic for multiple runs
Thanks, really appreciate. Why is the underestimated variance better in your opinion?
May
30
comment Mathematical function for weighting results
Another update: if I know, taht maximum difference is D, how can I make this equal to 1, using the above function, and rescale all the other, smaller values respectively?
May
30
comment Mathematical function for weighting results
Thank you very much Ilya. I further noticied that the basic function quite quickly reaches "high" weight values. if x and y differ by only 1, the value would still be at around 0.6. My values would most likely differ by 10-20, or even more, so I would like to be able to see big difference between 20 and 200, if that is the case. Should I simply divide the difference by 100 or other constant of mine or is there a better way to achieve what I described? thanks
May
10
comment geometric sum with probabilities
pi are pretty much random...
May
10
comment geometric series for fractional n
@Patrick OK, what you defined is the formula for annuity. it has a solution in the form A/r(1 - 1/(1+r)^n). Could I simply substitute fractional n now?
May
10
comment geometric series for fractional n
How to do that? That is precisely my question
May
9
comment orthogonal eigenvectors
What do you mean by "setting up"? Is there some common technique to achive a singular matrix?
May
8
comment orthogonal eigenvectors
Thank you very much for your answers. Could anyone state whether they are orthogonal in PCA case?
May
6
comment gradient descent rule
but perceptron is linear, isn't it?
May
6
comment gradient descent rule
well, for instance, in perceptron, you have w*x as your output. as you differentiate with respect to w_i you get set of Linear Equations. I guess maybe you meant full NN with hidden layers?
May
6
comment gradient descent rule
my bad... spelling was not an issue, but funny comment indeed :)
May
4
comment Concavity proof help
got it ! thanks a lot :)
May
4
comment Concavity proof help
OK, what about the Right hand side? the right hand side has ax^2, not a^2x^2
May
4
comment Concavity proof help
I modified my question to include my desired formula
May
4
comment Concavity proof help
I know, would you attempt solving it using the frmula in my comment?
May
4
comment Concavity proof help
Well, I meant if anyone could show it using the formula in my comment I would really appreciate it....
May
4
comment Concavity proof help
Sorry, I meant concave... It does not seem to work. I know that f(ax + (1-a)y) >= af(x) + (1-a)f(y)