11/01/2011

Naive Byes classification makes lots of assumption and still works well. Though the probability estimates is of low quality, relative ordering of the class probabilities are correct i.e its classification decisions are good.

-Bharath

11/03/2011

To accomplish Diversity in Feature Selection

apply Mutual Information process twice.

As normal, calculate Mutual Information of word

and class. Do sort. Then determine next ranked

by computing the mutual Information from next highly

ranked word and the previous word.

 

-- 

Nikhil 

10/1/11

To emphasize a point,

NBC with feature selection does

reduce the over-fitting problem with respect to f-measure.

 

Reason: No longer fixated on irrelevant features

as in case of taking almost every feature

Also:Fewer features has, of course ,faster calculation time.

 

M.

11/01/2011

NBC will compute probability highly erroneously, what matters is not the exact probability but the relative order. There are more cases where probabilities are wrong and relative order is correct.

-- Dinu

10/27/2010

In NBC we learn probabilities from the data and it computes posterior probability distribution on the class. Here the assumption is classes directly cause attributes and there is no intermediary.

-- Dinu

11/01/2011

Feedback detection can be intrusive and non-intrusive.
Intrusive detection is in a way explicitly asking the user to rate the items.
On the other hand, in non-intrusive detection, we follow the user actions.

-Sandeep Gautham

11/1/2011

NBC makes an assumption that all the attributes are independent of each other.
This reduces the computation of probabilities from n*d^k to n*d*k.

Where n is the number of classes, k is the number of attributes and d is the different values each of these attributes can take.

-Sandeep Gautham