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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
30
asked Mathematical function for weighting results
May
28
accepted generative vs discriminative machine learning
May
13
awarded  Tumbleweed
May
11
accepted Discriminant vs. generatve functions
May
11
accepted orthogonal eigenvectors
May
11
accepted orthogonal projection - simple exalanation needed
May
11
accepted Bayes theorem probability question
May
11
accepted geometric series for fractional n
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
10
asked geometric series for fractional n
May
10
asked geometric sum with probabilities
May
9
comment orthogonal eigenvectors
What do you mean by "setting up"? Is there some common technique to achive a singular matrix?
May
8
awarded  Commentator
May
8
comment orthogonal eigenvectors
Thank you very much for your answers. Could anyone state whether they are orthogonal in PCA case?
May
8
asked orthogonal eigenvectors
May
8
asked orthogonal projection - simple exalanation needed
May
8
asked generative vs discriminative machine learning