The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Virgiliu Pavlu - One of the best experts on this subject based on the ideXlab platform.
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the Maximum Entropy Method for analyzing retrieval measures
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
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SIGIR - The Maximum Entropy Method for analyzing retrieval measures
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
Xurong Chen - One of the best experts on this subject based on the ideXlab platform.
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Pion Valence Quark Distributions from Maximum Entropy Method
Physics Letters B, 2020Co-Authors: Hanyang Xing, Xiaopeng Wang, Qiang Fu, Rong Wang, Xurong ChenAbstract:Abstract Valence quark distributions of pion at very low resolution scale Q 0 2 ∼ 0.1 GeV2 are deduced from a Maximum Entropy Method, under the assumption that pion consists of only a valence quark and a valence anti-quark at such a low scale. Taking the obtained initial quark distributions as the nonperturbative input in the modified Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (with the GLR-MQ-ZRS corrections) evolution, the generated valence quark distribution functions at high Q 2 are consistent with the measured ones from a Drell-Yan experiment. The Maximum Entropy Method is also applied to estimate the valence quark distributions at relatively higher Q 2 = 0.26 GeV 2 . At this higher scale, other components (sea quarks and gluons) should be considered in order to match the experimental data. The first three moments of pion quark distributions at high Q 2 are calculated and compared with the other theoretical predictions.
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Valence quark distributions and structure function of the free neutron from Maximum Entropy Method
arXiv: High Energy Physics - Phenomenology, 2018Co-Authors: Qiang Fu, Xurong ChenAbstract:As free neutron targets are not available, the structure function $F_2^{n}$ of neutron can only be extracted by inclusive deuteron and proton deep-inelastic scattering data. We apply the Maximum Entropy Method to determine valence quark distributions and structure function of the free neutron at very low resolution scale $Q_0^2$. The structure function ratio $F_2^{n}/ F_{2}^{p}$ getting from this Method is approximatively consistent with experimental data. The $F_2^{n}/ F_{2}^{p}$ and d/u ratio calculated by Maximum Entropy Method are in accordance with theoretical models, such as the Diquark, Feynman, Quark Model and Isgur under the limit of x$\rightarrow$1. Maximum Entropy Method expect to be particularly useful in the study of hadronic states.
Javed A Aslam - One of the best experts on this subject based on the ideXlab platform.
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the Maximum Entropy Method for analyzing retrieval measures
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
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SIGIR - The Maximum Entropy Method for analyzing retrieval measures
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
Emine Yilmaz - One of the best experts on this subject based on the ideXlab platform.
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the Maximum Entropy Method for analyzing retrieval measures
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
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SIGIR - The Maximum Entropy Method for analyzing retrieval measures
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, 2005Co-Authors: Javed A Aslam, Emine Yilmaz, Virgiliu PavluAbstract:We present a model, based on the Maximum Entropy Method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our Methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the Maximum Entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting Maximum Entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the Maximum Entropy Method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding Maximum Entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.
Qiang Fu - One of the best experts on this subject based on the ideXlab platform.
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Pion Valence Quark Distributions from Maximum Entropy Method
Physics Letters B, 2020Co-Authors: Hanyang Xing, Xiaopeng Wang, Qiang Fu, Rong Wang, Xurong ChenAbstract:Abstract Valence quark distributions of pion at very low resolution scale Q 0 2 ∼ 0.1 GeV2 are deduced from a Maximum Entropy Method, under the assumption that pion consists of only a valence quark and a valence anti-quark at such a low scale. Taking the obtained initial quark distributions as the nonperturbative input in the modified Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (with the GLR-MQ-ZRS corrections) evolution, the generated valence quark distribution functions at high Q 2 are consistent with the measured ones from a Drell-Yan experiment. The Maximum Entropy Method is also applied to estimate the valence quark distributions at relatively higher Q 2 = 0.26 GeV 2 . At this higher scale, other components (sea quarks and gluons) should be considered in order to match the experimental data. The first three moments of pion quark distributions at high Q 2 are calculated and compared with the other theoretical predictions.
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Valence quark distributions and structure function of the free neutron from Maximum Entropy Method
arXiv: High Energy Physics - Phenomenology, 2018Co-Authors: Qiang Fu, Xurong ChenAbstract:As free neutron targets are not available, the structure function $F_2^{n}$ of neutron can only be extracted by inclusive deuteron and proton deep-inelastic scattering data. We apply the Maximum Entropy Method to determine valence quark distributions and structure function of the free neutron at very low resolution scale $Q_0^2$. The structure function ratio $F_2^{n}/ F_{2}^{p}$ getting from this Method is approximatively consistent with experimental data. The $F_2^{n}/ F_{2}^{p}$ and d/u ratio calculated by Maximum Entropy Method are in accordance with theoretical models, such as the Diquark, Feynman, Quark Model and Isgur under the limit of x$\rightarrow$1. Maximum Entropy Method expect to be particularly useful in the study of hadronic states.