Results 281 to 290 of about 1,093,151 (324)
The Effectiveness of Instructor Course of Basic Maritime Emergency Care on Knowledge and Skill Among Laypersons: A Mixed Methods Design. [PDF]
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Proceedings of the 24th international conference on Machine learning, 2007
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning.
Zhe Cao +4 more
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The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning.
Zhe Cao +4 more
openaire +1 more source
Proceedings of the 29th on Hypertext and Social Media, 2018
Software robots, or simply bots, have often been regarded as harmless programs confined within the cyberspace. However, recent events in our society proved that they can have important effects on real life as well. Bots have in fact become one of the key tools for disseminating information through online social networks (OSNs), influencing their ...
Perna, Diego, Tagarelli, Andrea
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Software robots, or simply bots, have often been regarded as harmless programs confined within the cyberspace. However, recent events in our society proved that they can have important effects on real life as well. Bots have in fact become one of the key tools for disseminating information through online social networks (OSNs), influencing their ...
Perna, Diego, Tagarelli, Andrea
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2017
In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of ...
Roffo, Giorgio, Melzi, Simone
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In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of ...
Roffo, Giorgio, Melzi, Simone
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search.
Jingfang Xu, Xing Li
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Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search.
Jingfang Xu, Xing Li
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2019
In the last decade food understanding has become a very attractive topic. This has implied the growing demand of Computer Vision algorithms for automatic diet assessment to treat or prevent food related diseases. However, the intrinsic variability of food, makes the research in this field incredibly challenging.
Allegra D. +6 more
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In the last decade food understanding has become a very attractive topic. This has implied the growing demand of Computer Vision algorithms for automatic diet assessment to treat or prevent food related diseases. However, the intrinsic variability of food, makes the research in this field incredibly challenging.
Allegra D. +6 more
openaire +2 more sources
Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
There have been great needs to develop effective methods for combining multiple rankings from heterogeneous domains into one single rank list arising from many recent web search applications, such as integrating web search results from multiple engines, facets, or verticals. We define this problem as Learning to blend rankings from multiple domains. We
Zhenzhen Kou +3 more
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There have been great needs to develop effective methods for combining multiple rankings from heterogeneous domains into one single rank list arising from many recent web search applications, such as integrating web search results from multiple engines, facets, or verticals. We define this problem as Learning to blend rankings from multiple domains. We
Zhenzhen Kou +3 more
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Information Retrieval, 2005
New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best ...
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New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best ...
openaire +1 more source
Proceedings of the 24th International Conference on World Wide Web, 2015
Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences between complete result rankings. Both types of approaches have
Yiwei Chen, Katja Hofmann
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Online learning to rank holds great promise for learning personalized search result rankings. First algorithms have been proposed, namely absolute feedback approaches, based on contextual bandits learning; and relative feedback approaches, based on gradient methods and inferred preferences between complete result rankings. Both types of approaches have
Yiwei Chen, Katja Hofmann
openaire +1 more source