--- On Sat, 10/25/08, Mark Waser <[EMAIL PROTECTED]> wrote: > > Scientists choose experiments to maximize information > > gain. There is no > > reason that machine learning algorithms couldn't > > do this, but often they don't. > > Heh. I would say that scientists attempt to do this and > machine learning > algorithms should do it. > > So where is the difference other than in the quality of > implementation (i.e. > "other than who performs it, of course").
There is no difference. I originally distinguished machine learning because all of the usual algorithms depend on minimizing the complexity of the hypothesis space. For example, we use neural networks with the minimum number of connections to learn the training data because we want to avoid over fitting. Likewise, decision trees and rule learning algorithms like RIPPER try to find the minimum number of rules that fit the data. I knew about clustering algorithms, but not why they worked. I learned all these different strategies for various algorithms in a machine learning course I took, but was unaware of the general principle and the reasoning behind it until I learned about AIXI. -- Matt Mahoney, [EMAIL PROTECTED] ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
