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Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation

https://doi.org/10.1145/3416508.3417121
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00500-017-2945-4openalex-snapshot
On the value of parameter tuning in heterogeneous ensembles effort estimation
inferredcites →https://doi.org/10.1007/s10664-011-9187-3openalex-snapshot
Using tabu search to configure support vector regression for effort estimation
inferredcites →https://doi.org/10.1007/s10664-018-9647-0openalex-snapshot
An ensemble-based model for predicting agile software development effort
inferredcites →https://doi.org/10.1007/s10664-019-09686-wopenalex-snapshot
A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation
inferredcites →https://doi.org/10.1007/s13721-016-0125-6openalex-snapshot
A review of automatic selection methods for machine learning algorithms and hyper-parameter values
inferredcites →https://doi.org/10.1016/j.infsof.2010.05.009openalex-snapshot
GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation
inferredcites →https://doi.org/10.1016/j.infsof.2011.09.002openalex-snapshot
Systematic literature review of machine learning based software development effort estimation models
inferredcites →https://doi.org/10.1016/j.infsof.2011.12.008openalex-snapshot
Evaluating prediction systems in software project estimation
inferredcites →https://doi.org/10.1016/j.infsof.2016.01.003openalex-snapshot
Exact Mean Absolute Error of Baseline Predictor, MARP0
inferredcites →https://doi.org/10.1016/j.infsof.2016.04.017openalex-snapshot
Tuning for software analytics: Is it really necessary?
inferredcites →https://doi.org/10.1016/j.jss.2015.11.040openalex-snapshot
The usage of ISBSG data fields in software effort estimation: A systematic mapping study
inferredcites →https://doi.org/10.1037//0033-2909.112.1.155openalex-snapshot
A power primer.
inferredcites →https://doi.org/10.1037/0033-2909.112.1.155openalex-snapshot
A power primer.
inferredcites →https://doi.org/10.1080/00401706.1970.10488634openalex-snapshot
Ridge Regression: Biased Estimation for Nonorthogonal Problems
inferredcites →https://doi.org/10.1080/00401706.1970.10488635openalex-snapshot
Ridge Regression: Applications to Nonorthogonal Problems
inferredcites →https://doi.org/10.1109/health.2003.1218710openalex-snapshot
Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717)
inferredcites →https://doi.org/10.1109/metric.2003.1232464openalex-snapshot
Dealing with missing software project data
inferredcites →https://doi.org/10.1109/tnn.2002.1031955openalex-snapshot
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
inferredcites →https://doi.org/10.1109/tse.2011.55openalex-snapshot
Data Mining Techniques for Software Effort Estimation: A Comparative Study
inferredcites →https://doi.org/10.1109/tse.2018.2794977openalex-snapshot
The Impact of Automated Parameter Optimization on Defect Prediction Models
inferredcites →https://doi.org/10.1109/tse.2019.2945020openalex-snapshot
How to “DODGE” Complex Software Analytics
inferredcites →https://doi.org/10.1145/1868328.1868335openalex-snapshot
How effective is Tabu search to configure support vector regression for effort estimation?
inferredcites →https://doi.org/10.1145/2487575.2487629openalex-snapshot
Auto-WEKA
inferredcites →https://doi.org/10.1145/2499393.2499394openalex-snapshot
The impact of parameter tuning on software effort estimation using learning machines
inferredcites →https://doi.org/10.1145/2639490openalex-snapshot
Proceedings of the 10th International Conference on Predictive Models in Software Engineering
inferredcites →https://doi.org/10.1145/2639490.2639510openalex-snapshot
The potential benefit of relevance vector machine to software effort estimation
inferredcites →https://doi.org/10.1145/2884781.2884857openalex-snapshot
Automated parameter optimization of classification techniques for defect prediction models
inferredcites →https://doi.org/10.1145/3295700openalex-snapshot
Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap Resampling
inferredcites →https://doi.org/10.1201/b10724openalex-snapshot
COSMIC Function Points
inferredcites →https://doi.org/10.2307/1271436openalex-snapshot
Ridge Regression: Biased Estimation for Nonorthogonal Problems
inferredcites →https://doi.org/10.2307/2529204openalex-snapshot
A Cluster Analysis Method for Grouping Means in the Analysis of Variance
inferredcites →https://doi.org/10.3906/elk-1809-129openalex-snapshot
Performance tuning for machine learning-based software development effort prediction models
inferredcites →https://doi.org/10.7551/mitpress/4175.001.0001openalex-snapshot
Learning with Kernels

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inferred← citeshttps://doi.org/10.1007/978-3-030-72660-7_56openalex-snapshot
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