5. The method of claim 4 , wherein said defining comprises: defining h ij as h ij = exp ⁢ ( -  f ⁡ ( x i , w ) - f ⁡ ( r ⁡ ( j ) , w )  2 2 ⁢ σ 2 ) , where x i =a feature vector containing evaluated feature information for document i of the set of documents, r(j)=the j-th document in the ranking induced by the relevance function, w=a coefficient vector, f(x i , w)=the relevance function evaluated on document i, and σ=a smoothing length-scale parameter.
5. The method of claim 4 , wherein said defining comprises: defining h ij as h ij = exp ⁢ ( -  f ⁡ ( x i , w ) - f ⁡ ( r ⁡ ( j ) , w )  2 2 ⁢ σ 2 ) , where x i =a feature vector containing evaluated feature information for document i of the set of documents, r(j)=the j-th document in the ranking induced by the relevance function, w=a coefficient vector, f(x i , w)=the relevance function evaluated on document i, and σ=a smoothing length-scale parameter. 7. The method of claim 5 , further comprising: adjusting the σ parameter and repeating said determining values for the coefficients of the relevance function that substantially minimize a sum over a set of queries of the defined query error if the relevance function does not satisfy said testing.