tag:blogger.com,1999:blog-19803222.post4986175089813985432..comments2024-03-18T01:45:45.724-06:00Comments on natural language processing blog: Classification weirdness, regression simplicityhalhttp://www.blogger.com/profile/02162908373916390369noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-19803222.post-6617531577801015022010-07-07T00:16:20.213-06:002010-07-07T00:16:20.213-06:00ACER Travelmate 4002lmi battery
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I guess I maybe said more than I'm willing to really defend, but certainly a hard margin SVM, the higher the norm, the smaller the margin, and hence the "harder." That's the intuition I was building on. I still vaguely stand behind it since we often use norm of w as a surrogate for complexity, which is a surrogate for difficulty.halhttps://www.blogger.com/profile/02162908373916390369noreply@blogger.comtag:blogger.com,1999:blog-19803222.post-58801247518549358442010-04-04T12:32:50.355-06:002010-04-04T12:32:50.355-06:00How about just looking at the amount of informatio...How about just looking at the amount of information provided by a certain feature - as considered by the model? If the model is probabilistic, all is well.Aleks Jakulinhttps://www.blogger.com/profile/14056353067485759304noreply@blogger.comtag:blogger.com,1999:blog-19803222.post-86615161774068100032010-04-03T16:50:03.635-06:002010-04-03T16:50:03.635-06:00I think I am missing something too. In regression ...I think I am missing something too. In regression by scaling the weights you change the slope. In classification scaling the weights changes the slope too, but the zero level-set stays the same. How is that if you make it insensitive to such changes you loose information? How does the magnitude of the weight vector relate to "hardness"?Samnoreply@blogger.comtag:blogger.com,1999:blog-19803222.post-14739555761963991412010-04-01T09:54:23.639-06:002010-04-01T09:54:23.639-06:00I'm a little confused...a classifier can be re...I'm a little confused...a classifier can be rescaled and perform the same (measured on 0-1 loss), but when learning, the training criterion may care about the scale (0-1 loss doesn't care, log loss does). So therefore it seems that a magnitude weight 5 does kind of mean something, if you're using something like log loss. What have I missed?Chrishttps://www.blogger.com/profile/02873949286995651782noreply@blogger.com