Skip to content

L1-norm and L2-norm regularization doc #3586

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 12 commits into from
May 28, 2019
Merged

L1-norm and L2-norm regularization doc #3586

Changes from 1 commit
Commits
Show all changes
12 commits
Select commit Hold shift + click to select a range
1cb88a0
L1-norm and L2-norm regularization doc
wschin Apr 25, 2019
ad3d98e
Address comments
wschin Apr 25, 2019
a05da4a
Shared content
wschin Apr 25, 2019
31eaa2d
reuse content
wschin Apr 25, 2019
b7c88c6
Address comments
wschin Apr 25, 2019
4cca10d
Update docs/api-reference/regularization-l1-l2.md
natke Apr 26, 2019
5c95b53
Update docs/api-reference/regularization-l1-l2.md
natke Apr 26, 2019
8feae96
Update regularization-l1-l2.md
najeeb-kazmi Apr 26, 2019
bf948b6
Clear
wschin Apr 26, 2019
74e81ff
Merge branch 'l1l2' of github.com:wschin/machinelearning into l1l2
wschin Apr 26, 2019
2cb578a
Merge branch 'master' into l1l2
wschin May 24, 2019
0c7d9f9
Explain ER and loss
wschin May 24, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
Update docs/api-reference/regularization-l1-l2.md 
Co-Authored-By: wschin <[email protected]>
  • Loading branch information
@natke @wschin
natke and wschin authored Apr 26, 2019
commit 5c95b53c6aa3387f4f09915d03ba28a5056b7ee1
2 changes: 1 addition & 1 deletion docs/api-reference/regularization-l1-l2.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
This class use [empricial risk minimization](https://en.wikipedia.org/wiki/Empirical_risk_minimization)
This class use [empirical risk minimization](https://en.wikipedia.org/wiki/Empirical_risk_minimization)
to formulate the optimization problem built upon collected data.
If the training data does not contain enough data points
(for example, to train a linear model in $n$-dimensional space, we need at least $n$ data points),
Expand Down