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<urlset xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:image="http://www.google.com/schemas/sitemap-image/1.1" xsi:schemaLocation="http://www.sitemaps.org/schemas/sitemap/0.9 http://www.sitemaps.org/schemas/sitemap/0.9/sitemap.xsd"><url><loc>https://burakhimmetoglu.com/blogs/</loc><lastmod>2020-12-06T16:13:24+00:00</lastmod><changefreq>weekly</changefreq><priority>0.6</priority></url><url><loc>https://burakhimmetoglu.com/deterministic-randomness/</loc><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/11/brain-2062057_1920.jpg</image:loc><image:title>brain-2062057_1920</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/11/abacus-358569_1920.jpg</image:loc><image:title>abacus-358569_1920</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/11/meandlev.jpeg</image:loc><image:title>meandlev</image:title></image:image><lastmod>2020-03-19T06:25:03+00:00</lastmod><changefreq>weekly</changefreq><priority>0.6</priority></url><url><loc>https://burakhimmetoglu.com/2016/12/01/stacking-models-for-improved-predictions/</loc><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/12/table.png</image:loc><image:title>table</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/12/stackplot.png</image:loc><image:title>stackplot</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/12/models.png</image:loc><image:title>models</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/12/workflow.png</image:loc><image:title>workflow</image:title></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2016/12/stacking.png</image:loc><image:title>stacking</image:title></image:image><lastmod>2019-01-07T17:23:12+00:00</lastmod><changefreq>monthly</changefreq></url><url><loc>https://burakhimmetoglu.com/2019/01/07/pancake/</loc><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/12/pancake-640869_1280-e1546039519258.jpg</image:loc><image:title>pancake-640869_1280</image:title></image:image><lastmod>2019-01-07T07:09:26+00:00</lastmod><changefreq>monthly</changefreq></url><url><loc>https://burakhimmetoglu.com/2018/09/19/an-overview-of-feature-selection-strategies/</loc><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/lsvt_table2.png</image:loc><image:title>lsvt_table</image:title><image:caption>Results for LSVT data</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/small_table1.png</image:loc><image:title>small_table</image:title><image:caption>Comparison of performance metric for the two synthetic datasets.</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/feats_comparison-e1536552812596.png</image:loc><image:title>feats_comparison</image:title><image:caption>Feature weights (rescaled) from each   selection algorithm</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/overlap.png</image:loc><image:title>overlap</image:title><image:caption>The red vertical dotted line is the weighted average of the means of + and - classes. Higher overlap corresponds to a case when the + and - distributions are close to each other.</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/lsvt_feats1.png</image:loc><image:title>LSVT_feats</image:title><image:caption>P-value and overlap for each feature in the LSVT dataset</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/fs_algo-e1536443490231.jpeg</image:loc><image:title>FS_algo</image:title><image:caption>Pseudo-code forward feature selection algorithm.</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/ffs1.png</image:loc><image:title>FFS</image:title><image:caption>Fig.2 Forward feature selection procedure. The desired number of features is obtained when CV score is maximized.</image:caption></image:image><image:image><image:loc>https://burakhimmetoglu.com/wp-content/uploads/2018/09/lasso_and_ridge-e1536377993337.jpeg</image:loc><image:title>lasso_and_ridge</image:title><image:caption>Fig.1 LASSO (left) and Ridge (right) regression feature weight 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