Add to ORKGThe present invention concerns a novel method to efficiently score documents (texts, images, audios, videos, and any other information file) by using a machine-learned ranking function modeled by an additive ensemble of regression trees. A main contribution is a new representation of the tree ensemble based on bitvectors, where the tree traversal, aimed to detect the leaves that contribute to the final scoring of a document, is performed through efficient logical bitwise operations. In addition, the traversal is not performed one tree after another, as one would expect, but it is interleaved, feature by feature, over the whole tree ensemble. Tests conducted on publicly available LtR datasets confirm unprecedented speedups (up to 6.5×) over ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
The present invention concerns a novel method to efficiently score documents (texts, images, audios,...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality docume...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality docu...
Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
The present invention concerns a novel method to efficiently score documents (texts, images, audios,...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality docume...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality docu...
Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...