Difference between revisions of "Tips"
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* If you want to benchmark your computer for Matlab speed simply run "bench" in matlab | * If you want to benchmark your computer for Matlab speed simply run "bench" in matlab | ||
* By default Matlab only allocates about 117MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions [http://www.mathworks.com/support/solutions/data/1-18I2C.html?solution=1-18I2C here]. See also the general memory instructions [http://www.mathworks.com/support/tech-notes/1100/1106.html here]. | * By default Matlab only allocates about 117MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions [http://www.mathworks.com/support/solutions/data/1-18I2C.html?solution=1-18I2C here]. See also the general memory instructions [http://www.mathworks.com/support/tech-notes/1100/1106.html here]. | ||
| + | * Remember that the Measure (and error function you use) strongly influence the quality and fit of the surrogate model. If you are unhappy with the final model, try a different Measure and/or error function. | ||
Revision as of 18:34, 24 September 2007
- The matlab neural network implementation is very slow! Try to avoid using crossvalidation but use test samples instead (see the FAQ entry).
- if you use the RBF neural network model type and you get a crash in "newrb" this is an error in the matlab toolbox implementation and not anything we can do about (a workaround is available on request). This should be fixed by Matlab 7.5
- you can switch off adaptive sample selection if you do not specify a <SampleSelector> tag. See Adaptive Modeling Mode.
- If you want to benchmark your computer for Matlab speed simply run "bench" in matlab
- By default Matlab only allocates about 117MB memory space for the Java Virtual Machine. If you would like to increase this limit (which you should) please follow the instructions here. See also the general memory instructions here.
- Remember that the Measure (and error function you use) strongly influence the quality and fit of the surrogate model. If you are unhappy with the final model, try a different Measure and/or error function.
