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Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.
Experiments showed the effectiveness of the proposed approach in reducing the training time for both methods considerably while achieving a similar accuracy to the standard SVM.
Experimental results show that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.
Proceedings ArticleDOI
Xiaolong Zhang, Fang Ren 
18 Oct 2008
15 Citations
The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.
Experimental results show its efficiency compared to two-class SVM algorithms while producing results of similar accuracy.

Related Questions

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How can we improve the accuracy of forecast models?5 answersTo improve the accuracy of forecast models, various approaches can be employed. One method is to use nonlinear vector autoregressive models based on global approximators, such as neural networks, Volterra, and Weiner series. These models have been found to be more accurate than linear models based on root mean square error and model confidence set criteria. Another approach is to utilize neural network techniques to model the relationships between predictors and predictands, especially for extended range forecasts. These techniques can extract more sophisticated pattern relationships and covariabilities, leading to improved forecast accuracy. Additionally, aggregating forecasts through compromise forecasts, which are the average predictions in a group, has been shown to harness collective predictive intelligence and improve accuracy. Lastly, explicitly accounting for cross-immunity in mathematical models used for forecasting during infectious disease outbreaks, such as influenza, can enhance forecast accuracy.
How can we improve the accuracy of machine learning models?5 answersTo improve the accuracy of machine learning models, several approaches can be taken. One approach is to integrate machine learning with development and operations (DevOps), which allows for seamless model upgrading and simplified management and monitoring. Another approach is to optimize the training data by using various data optimization techniques, considering issues such as data quality, missing values, and non-uniformity in features. Additionally, the use of techniques like the Synthetic Minority Over-sampling Technique can help improve the accuracy of machine learning models when dealing with imbalanced training datasets. Furthermore, dimension reduction techniques can be applied to improve the performance of machine learning models, especially when dealing with high-dimensional datasets. These approaches can collectively contribute to enhancing the accuracy of machine learning models.
What are some ways to improve the accuracy of data analysis?5 answersTo improve the accuracy of data analysis, various techniques can be utilized. One approach is to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. Another method is manual curation, which involves obtaining new compounds from published works and paying attention to stereochemistry. Dealing with imbalanced data, techniques such as random over sampling, random under sampling, and hybrid approaches can be considered. Additionally, pre-processing techniques like missing value replacement and manual feature selection based on human expertise can be applied to improve accuracy.
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