Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
Use this tag for any use of optimization within statistics.
1
vote
1
answer
76
views
How can I make this biological relation into a glm model?
I have a biological relation:
Y/m = (X * b) / (1 + X * b)
where Y and X are variables, m and b are parameters. m is greater than Y, and everything is greater than 0.
I have some training data with …
9
votes
2
answers
2k
views
Why does adding L1 penalty to R's optim slows things down so much (relative to no penalty or...
A quick Google search turned up a package call "lbfgs" which "finds the optimum of an objective plus the L1 norm of the problem’s parameters" with "a fast and memory-efficient implementation of these optimization …
2
votes
0
answers
219
views
When is logistic regression minimizing under squared error loss the same as maximizing binom... [duplicate]
Implementing logistic regression and getting different results depending on whether I minimize squared error or maximize log likelihood. When are the two equivalent?
1
vote
1
answer
523
views
Neural networks: how can convex optimization produce different weights each time?
However the optimization function (sum squared error in my case) is convex, which means there is one global minima. So I suppose this minima has several equivalent sets of weights that map to it. …