Also, I feel like akinator.com is using a Bayesian tree. Would you confirm this? What algorithm do you think they are using?
I thought PG's explanation in "A Plan for Spam" was pretty good: http://www.paulgraham.com/naivebayes.html
Also, I feel like akinator.com is using a Bayesian tree. Would you confirm this?
It's possible, but I doubt it.
What algorithm do you think they are using?
A plain binary search tree would work perfectly. I guess it could be broken by people entering deliberately wrong data, but..
It's clearly some kind of search tree, because the questions are influenced by previous answers.
You could do a Bayesian version, but I'm not sure it would perform any better.
I'm trying to list the usages of Bayesian inferences in industry. So, there is spam filtering, I bet there would be recommandation engines (ex: amazon's product recommandation, ad targeting). Would you know of other uses?
Do you know of some kind of tutorial of how one particular bayesian engine is applied to a particular dataset in a commercial venture?
Also, I have been looking at weka (http://www.cs.waikato.ac.nz/ml/weka/), and j48 in particular. j48 is an implementation of the C4.5 algorithm, (which is close to ID3 and C5). Here is how I learned about C4.5: http://www.cs.umd.edu/~samir/498/10Algorithms-08.pdf. Would you happen to know if one of these algorithms have commercial applications?
In a different subject, Judea Pearl has been awarded the Turing award for using Bayesian networks to calculate probability of causality. Which is different than traditional Bayesian analysis, where we calculate probabilities of a property being true. In his theory, he claims one can calculate the probability of a property being a necessary condition, the probability of a sufficient condition to another property (and the probability of both combined). I'm really having trouble understanding the ins and outs of this too. Have you heard about an usage of this theory? Do you think it has a viable shot at having commercial applications?
