{"id":181,"date":"2006-04-06T17:21:32","date_gmt":"2006-04-06T23:21:32","guid":{"rendered":"http:\/\/hunch.net\/?p=181"},"modified":"2006-04-08T16:57:19","modified_gmt":"2006-04-08T22:57:19","slug":"bounds-greater-than-1","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=181","title":{"rendered":"Bounds greater than 1"},"content":{"rendered":"<p><a href=\"http:\/\/www.cs.toronto.edu\/~nati\/\">Nati Srebro<\/a> and <a href=\"http:\/\/www.cs.uwaterloo.ca\/~shai\/\">Shai Ben-David<\/a> have a <a href=\"http:\/\/www.cs.toronto.edu\/~nati\/Publications\/SrebroBenDavidCOLT06.pdf\">paper<\/a> at <a href=\"http:\/\/learningtheory.org\/colt2006\/\">COLT<\/a> which, in the appendix, proves something very striking: several previous error bounds are <em>always<\/em> greater than 1.<\/p>\n<p><strong>Background<\/strong> One branch of learning theory focuses on theorems which<\/p>\n<ol>\n<li>Assume samples are drawn IID from an unknown distribution <em>D<\/em>.<\/li>\n<li>Fix a set of classifiers<\/li>\n<li>Find a high probability bound on the maximum true error rate (with respect to <em>D<\/em>) as a function of the empirical error rate on the training set.\n<\/li>\n<\/ol>\n<p>Many of these bounds become extremely complex and hairy.  <\/p>\n<p><strong>Current<\/strong> Everyone working on this subject wants &#8220;tighter bounds&#8221;, however there are different definitions of &#8220;tighter&#8221;.  Some groups focus on &#8220;functional tightness&#8221; (getting the right functional dependency between the size of the training set and a parameterization of the hypothesis space) while <a href=\"http:\/\/www.neurocolt.com\/bounds2002.html\">others<\/a> focus on &#8220;practical tightness&#8221; (finding bounds which work well on practical problems).  (I am definitely in the second camp.)<\/p>\n<p>One of the dangers of striving for &#8220;functional tightness&#8221; is that the bound can depend on strangely interrelated parameters.  In fact, apparently these strange interrelations can become so complex that they end up <em>always<\/em> larger than 1 (some bounds <a href=\"http:\/\/www.jmlr.org\/papers\/volume5\/lanckriet04a\/lanckriet04a.pdf\">here<\/a> and <a href=\"http:\/\/www.kyb.mpg.de\/publications\/pss\/ps1999.ps\">here<\/a>).  <\/p>\n<p>It seems we should ask the question: &#8220;Why are we doing the math?&#8221;  If it is just done to get a paper accepted under review, perhaps this is unsatisfying.  The real value of math comes when it guides us in designing learning algorithms.  Math from bounds greater than 1 is a dangerously weak motivation for learning algorithm design.<\/p>\n<p>There is a significant danger in taking this &#8220;oops&#8221; too strongly.  <\/p>\n<ol>\n<li>There exist some reasonable arguments (not made here) for aiming at functional tightness.<\/li>\n<li>The value of the research a person does is more related to the best they have done than the worst.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Nati Srebro and Shai Ben-David have a paper at COLT which, in the appendix, proves something very striking: several previous error bounds are always greater than 1. Background One branch of learning theory focuses on theorems which Assume samples are drawn IID from an unknown distribution D. Fix a set of classifiers Find a high &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/hunch.net\/?p=181\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Bounds greater than 1&#8221;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29,18,8],"tags":[],"class_list":["post-181","post","type-post","status-publish","format-standard","hentry","category-machine-learning","category-papers","category-prediction-theory"],"_links":{"self":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=181"}],"version-history":[{"count":0,"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/181\/revisions"}],"wp:attachment":[{"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=181"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=181"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}