11. A method for combining multi-domain relevance measures provided by a plurality of weak rankers, the method comprising: defining an instance space that is comprised of a plurality of sets, each set being a set of instances provided by a distinct weak ranker of the plurality of the weak rankers, each instance of a set of instances being a sample tag from a media/tag pair, wherein a first weak ranker of the plurality of the weak rankers provides a first set of instances based on tag co-occurrence (“TC”) relevance measures, and a second weak ranker of the plurality of the weak rankers provides a second set of instances based on tag content correlation (“TCC”) relevance measures, and a third weak ranker of the plurality of the weak rankers provides a third set of instances based on image-conditioned tad correlation (“ITC”) relevance measures, wherein the ITC relevance measures are based at least in part on a distance between a target tag and at least one candidate tag, and wherein the distance is an asymmetric distance or a symmetric distance, and wherein the asymmetric distance defined as D ITC a ⁡ ( t i , t j , x ) =  L t i ⁡ ( x ) · L t j ⁡ ( x )  L t i ⁡ ( x )  - L t i ⁡ ( x )  where x is a target media instance of a target media/tag pair, and where L t i (x)=[ , ], and wherein the symmetric distance defined as D ITC s ⁡ ( t i , t j , x ) = L t i ⁡ ( x ) · L t j ⁡ ( x )  L t i ⁡ ( x )  ⁢  L t j ⁡ ( x )  where x is the target media instance, and where L t i (x)=[ , ] and, for both the asymmetric distance and the symmetric distance, where exponents of 1 indicate a unigram likelihood based on a VLM in a unigram form, and where exponents of 2 indicate a bigram likelihood based on a VLM in a bigram form, and where exponents of 3 indicate a trigram likelihood based on a VLM in a trigram form; generating, by a computing device, a mapping function for each weak ranker, each mapping function configured for mapping a corresponding instance from the instance space to a ranking space R; and mapping each instance using the corresponding mapping function from the instance space to a ranking space R.
11. A method for combining multi-domain relevance measures provided by a plurality of weak rankers, the method comprising: defining an instance space that is comprised of a plurality of sets, each set being a set of instances provided by a distinct weak ranker of the plurality of the weak rankers, each instance of a set of instances being a sample tag from a media/tag pair, wherein a first weak ranker of the plurality of the weak rankers provides a first set of instances based on tag co-occurrence (“TC”) relevance measures, and a second weak ranker of the plurality of the weak rankers provides a second set of instances based on tag content correlation (“TCC”) relevance measures, and a third weak ranker of the plurality of the weak rankers provides a third set of instances based on image-conditioned tad correlation (“ITC”) relevance measures, wherein the ITC relevance measures are based at least in part on a distance between a target tag and at least one candidate tag, and wherein the distance is an asymmetric distance or a symmetric distance, and wherein the asymmetric distance defined as D ITC a ⁡ ( t i , t j , x ) =  L t i ⁡ ( x ) · L t j ⁡ ( x )  L t i ⁡ ( x )  - L t i ⁡ ( x )  where x is a target media instance of a target media/tag pair, and where L t i (x)=[ , ], and wherein the symmetric distance defined as D ITC s ⁡ ( t i , t j , x ) = L t i ⁡ ( x ) · L t j ⁡ ( x )  L t i ⁡ ( x )  ⁢  L t j ⁡ ( x )  where x is the target media instance, and where L t i (x)=[ , ] and, for both the asymmetric distance and the symmetric distance, where exponents of 1 indicate a unigram likelihood based on a VLM in a unigram form, and where exponents of 2 indicate a bigram likelihood based on a VLM in a bigram form, and where exponents of 3 indicate a trigram likelihood based on a VLM in a trigram form; generating, by a computing device, a mapping function for each weak ranker, each mapping function configured for mapping a corresponding instance from the instance space to a ranking space R; and mapping each instance using the corresponding mapping function from the instance space to a ranking space R. 14. The method of claim 11 wherein at least one of the plurality of the weak rankers provides one set of instances in a semantic domain and a second one of the plurality of the weak rankers provides a second set of instances in a visual domain.