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One obvious task we might ask of a learning algorithm is to learn the
rank function. Following the Probability Ranking Principle §5.5.1 , we might attempt to learn the
probability that a document is relevant, given a set of features it
contains. Recall from Section §4.2.1
how RelFbk information can be used to modify a query vector so as
to move it closer to those documents a user has marked as relevant. The
same technique can be used to move documents towards the query! The
radically different consequence of this change is that while query
modification is only useful once to the user benefiting from it,
document modification changes the document's representations for all
subsequent users queries.
We will also be concerned with another task.
Rather than assigning a single real number to each document ($for
example), we shall attempt (using the same set of document features) to
classify a document into one of a small number of potential
categories. Most simply, we may be interested in BINARY
classification of documents into one of two categories. An obvious use
of binary classification would be to classify into Relevant and
NonRelevant classes. More complex classifications into one of $C$
different classes are also possible. For example, imagine that you would
like to have your Email client automatically sort your incoming Email
into various mail folders you have used historically. Having decided how
many classes to use, we must also determine whether our
ASSIGNMENT to these classes is binary or whether we provide the
probability that it belongs to a class. Classification techniques are
discussed in §7.4 .
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Alternative Tasks for Learning