The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Tang Yuan - One of the best experts on this subject based on the ideXlab platform.
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Research on Support Vector Stepwise Regression Algorithm & its Improvement
Computer Science, 2007Co-Authors: Zeng Shao, Tang YuanAbstract:According to the nature that Support Vectors are sparse and they are located near a hyper-plane, Support Vectors Stepwise Regression Algorithm is proposed and its method of constructing a new sample subset was improved in this paper. The m is optimized with Integer Programming, which is number of new samples extracted in every search. Finally, analyzed its complexity, and tested and verified convergence and availability of the algorithms by the simulation.
Zeng Shao - One of the best experts on this subject based on the ideXlab platform.
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Research on Support Vector Stepwise Regression Algorithm & its Improvement
Computer Science, 2007Co-Authors: Zeng Shao, Tang YuanAbstract:According to the nature that Support Vectors are sparse and they are located near a hyper-plane, Support Vectors Stepwise Regression Algorithm is proposed and its method of constructing a new sample subset was improved in this paper. The m is optimized with Integer Programming, which is number of new samples extracted in every search. Finally, analyzed its complexity, and tested and verified convergence and availability of the algorithms by the simulation.
Wei Gao - One of the best experts on this subject based on the ideXlab platform.
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ensemble and enhanced pm10 concentration forecast model based on Stepwise Regression and wavelet analysis
Atmospheric Environment, 2013Co-Authors: Yuanyuan Chen, Runhe Shi, Shijie Shu, Wei GaoAbstract:Abstract An ensemble and enhanced PM10 (particulate matter with a diameter less than 10 μm) concentration forecast model was established in eastern China based on data from 2005 to 2009. The enhanced model consists of a single Stepwise Regression forecast model and a combined forecast model based on wavelet decomposition and Stepwise Regression. Six individual forecast results were obtained with a combined model that can predict PM10 concentrations at multiple scales. By decomposing variables into detailed and approximated components in six scales and with the application of Stepwise Regression, the best-fitted forecast models were established in each component of the different scales. Then, the predicted results of the detail and approximation components were reconstructed in each scale as the enhanced prediction. A regional model was established for eastern China. The accuracy rate of each forecasted result by the regional model was calculated using testing data from 2010 based on the needs of operational forecasting. Precision evaluations were also performed. A comparatively higher accuracy was obtained by the combined model. The advantage of predicting the PM10 concentration with the combined model had wide spatial and temporal suitability. An enhanced forecast model was established for each city of eastern China with improvements, where all the predicted results in each city were evaluated by the accuracy rate and precision validation. In each city, the best-fitted model with the highest precision was selected and combined in an ensemble. The ensemble and enhanced forecast model had a significant improvement in accuracy rate and the highest precision of PM10 concentration forecasting in eastern China.
Zhang Huan - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Television Audience Rating Based on Fuzzy Cognitive Maps with Forward Stepwise Regression
International Journal of Pattern Recognition and Artificial Intelligence, 2017Co-Authors: Patrick S. P. Wang, Ying Zheng, Zhang HuanAbstract:The television audience rating is an important indicator of the quality of television programs and important reference for decision-television operator. As many factors that affect the ratings and the trends are complex, the article proposes a television rating mining predictive model based on fuzzy cognitive maps (FCMs) with forward Stepwise Regression. The FCMs use the causal relationship among various concept nodes to simulate the fuzzy reasoning, and enhance the dynamic behavior of the simulation system with its feedback mechanism, which is suitable for system to predict the trend of television audience rating. A FCM-based model for predicting television audience rating is proposed in this paper. The forward Stepwise Regression algorithm is used to obtain concept nodes of coarse weight matrix for FCMs, and then a training weight algorithm is used to refine the coarse weight matrix model. The FCM model is applied to mine the television audience rating, realizing to predict the television playback volum...
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Prediction of Television Audience Rating Based on Fuzzy Cognitive Maps with Forward Stepwise Regression
International Journal of Pattern Recognition and Artificial Intelligence, 2017Co-Authors: Patrick Wang, Ying Zheng, Zhang HuanAbstract:The television audience rating is an important indicator of the quality of television programs and important reference for decision-television operator. As many factors that affect the ratings and the trends are complex, the article proposes a television rating mining predictive model based on fuzzy cognitive maps (FCMs) with forward Stepwise Regression. The FCMs use the causal relationship among various concept nodes to simulate the fuzzy reasoning, and enhance the dynamic behavior of the simulation system with its feedback mechanism, which is suitable for system to predict the trend of television audience rating. A FCM-based model for predicting television audience rating is proposed in this paper. The forward Stepwise Regression algorithm is used to obtain concept nodes of coarse weight matrix for FCMs, and then a training weight algorithm is used to refine the coarse weight matrix model. The FCM model is applied to mine the television audience rating, realizing to predict the television playback volume. The experimental result shows that the modeling method is effective.
Paul W. Dickman - One of the best experts on this subject based on the ideXlab platform.
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Model selection in medical research: a simulation study comparing Bayesian model averaging and Stepwise Regression.
BMC medical research methodology, 2010Co-Authors: Anna Genell, Szilard Nemes, Gunnar Steineck, Paul W. DickmanAbstract:Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited. Bayesian model averaging may be useful for model selection but only limited attempts to compare it to Stepwise Regression have been published. We therefore performed a simulation study to compare Stepwise Regression with Bayesian model averaging.
