http://as-phy-radiorm.asc.ohio-state.edu/elog/Ideas Advice ELOG V3.1.5 http://as-phy-radiorm.asc.ohio-state.edu/elog/Ideas/elog.png http://as-phy-radiorm.asc.ohio-state.edu/elog/Ideas http://as-phy-radiorm.asc.ohio-state.edu/elog/Ideas/2 <p>Also, I should mention, SVM is a great way to not only build functions,<br /> but also visualise higher dimensions in 2D space:<br /> <a href="http://scikit-learn.org/stable/modules/svm.html">http://scikit-learn.org/stable/modules/svm.html</a><br /> (see 1.4.2. Regression)</p> <p>&nbsp;</p> <p>Yes, overfitting is a concern. But that will happen with 2 or 8 or 80 features if you have only a limited qty of triggers (events). Decreasing features does does not decrease overfitting. Only increasing the qty of events improves this condition.<br /> You can use cross-validation to compensate. This shuffles small dataset into larger, safer datasets:<br /> <a href="http://scikit-learn.org/stable/modules/cross_validation.html">http://scikit-learn.org/stable/modules/cross_validation.html</a>&nbsp;&nbsp;Download this app:&nbsp;<a href="http://www.nutonian.com/products/eureqa/">http://www.nutonian.com/products/eureqa/</a></p> Fri, 19 May 2017 12:42:01 -0400 http://as-phy-radiorm.asc.ohio-state.edu/elog/Ideas/1 <p>Ideas for improving the geometric filter:</p> Mon, 15 May 2017 14:00:43 -0400