Timeline for How to use features for training neural network?
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 6, 2017 at 2:14 | vote | accept | afagarap | ||
| Jul 17, 2017 at 4:58 | vote | accept | afagarap | ||
| Aug 6, 2017 at 2:14 | |||||
| Jul 17, 2017 at 4:57 | vote | accept | afagarap | ||
| Jul 17, 2017 at 4:58 | |||||
| Jul 17, 2017 at 4:57 | answer | added | afagarap | timeline score: 0 | |
| Jul 11, 2017 at 9:52 | comment | added | afagarap | $[a, b]$ is the scale range, for my problem, it's $[0.0, 1.0]$. | |
| Jul 11, 2017 at 9:51 | comment | added | afagarap | I actually defined it as a function $f(x) = \frac{(b - a) \times (x - min)}{(max - min)} + a$ where $[min, max] \rightarrow [a, b]$. | |
| Jul 11, 2017 at 9:15 | comment | added | Nikolas Rieble | If I understand correctly, a linear scaling here means first $x_i = x_i - min_i(x_i)$ and the $x_i = x_i / max_i(x_i)$ for all values. | |
| Jul 11, 2017 at 5:02 | comment | added | afagarap |
Thanks, @NikolasRieble I did read one paper, the other night. What they did was to index the symbolic features like service to [0, n-1] where n is the number of symbols. As for the integer values, they did a linear scaling to [0.0, 1.0]. What I am now having a trouble with is how they did the linear scaling.
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| Jul 10, 2017 at 9:18 | comment | added | Nikolas Rieble | If it is a publicly available dataset, i doubt that you are the first one to work on it. I recommend to read papers on how others preprocessed the same dataset | |
| Jul 9, 2017 at 10:55 | review | First posts | |||
| Jul 9, 2017 at 13:25 | |||||
| Jul 9, 2017 at 10:51 | history | asked | afagarap | CC BY-SA 3.0 |