Timeline for Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
Current License: CC BY-SA 3.0
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| Aug 30, 2017 at 6:35 | comment | added | user83346 | @user1761806 an element is a row. The notation is explained in the link at the beginning of the answer | |
| Aug 29, 2017 at 22:17 | comment | added | Reddspark | what is x and x_i? If my X is an input of 5 columns, and 100 rows, what would x and x_i be? | |
| Aug 29, 2017 at 19:26 | comment | added | Reddspark | "for each element of my training sample" -- is element here referring to a row or column (i.e. feature ) | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
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| Aug 22, 2015 at 7:42 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 22, 2015 at 7:37 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 21, 2015 at 14:58 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 21, 2015 at 4:09 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 20, 2015 at 18:08 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 20, 2015 at 17:53 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 20, 2015 at 17:26 | history | edited | user83346 | CC BY-SA 3.0 |
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| Aug 20, 2015 at 17:05 | history | answered | user83346 | CC BY-SA 3.0 |