The potential benefit of relevance vector machine to software effort estimation
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[1] Michael E. Tipping,et al. Analysis of Sparse Bayesian Learning , 2001, NIPS.
[2] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[3] Adam Trendowicz,et al. Handling Estimation Uncertainty with Bootstrapping: Empirical Evaluation in the Context of Hybrid Prediction Methods , 2011, 2011 International Symposium on Empirical Software Engineering and Measurement.
[4] Yong Hu,et al. Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..
[5] Martin J. Shepperd,et al. Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..
[6] Mark Harman,et al. Search-Based Software Project Management , 2014, Software Project Management in a Changing World.
[7] Stephen G. MacDonell,et al. Evaluating prediction systems in software project estimation , 2012, Inf. Softw. Technol..
[8] Xin Yao,et al. Software effort estimation as a multiobjective learning problem , 2013, TSEM.
[9] Ioannis Stamelos,et al. Software productivity and effort prediction with ordinal regression , 2005, Inf. Softw. Technol..
[10] Ioannis Stamelos,et al. Managing uncertainty in project portfolio cost estimation , 2001, Inf. Softw. Technol..
[11] Tim Menzies,et al. Active learning and effort estimation: Finding the essential content of software effort estimation data , 2013, IEEE Transactions on Software Engineering.
[12] Parag C. Pendharkar,et al. A probabilistic model for predicting software development effort , 2003, IEEE Transactions on Software Engineering.
[13] Lisa Werner,et al. Principles of forecasting: A handbook for researchers and practitioners , 2002 .
[14] Xin Yao,et al. The impact of parameter tuning on software effort estimation using learning machines , 2013, PROMISE.
[15] Barbara A. Kitchenham,et al. A Simulation Study of the Model Evaluation Criterion MMRE , 2003, IEEE Trans. Software Eng..
[16] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[17] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[18] Tim Menzies,et al. The \{PROMISE\} Repository of Software Engineering Databases. , 2005 .
[19] Ioannis Stamelos,et al. On the use of Bayesian belief networks for the prediction of software productivity , 2003, Inf. Softw. Technol..
[20] Tim Menzies,et al. On the Value of Ensemble Effort Estimation , 2012, IEEE Transactions on Software Engineering.
[21] Bojan Cukic,et al. Building a second opinion: learning cross-company data , 2013, PROMISE.
[22] Xin Yao,et al. Ensembles and locality: Insight on improving software effort estimation , 2013, Inf. Softw. Technol..
[23] Magne Jørgensen,et al. Evidence-based guidelines for assessment of software development cost uncertainty , 2005, IEEE Transactions on Software Engineering.
[24] Ioannis Stamelos,et al. A Simulation Tool for Efficient Analogy Based Cost Estimation , 2000, Empirical Software Engineering.
[25] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[26] Magne Jørgensen,et al. An effort prediction interval approach based on the empirical distribution of previous estimation accuracy , 2003, Inf. Softw. Technol..
[27] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[28] Silvio Romero de Lemos Meira,et al. Bagging Predictors for Estimation of Software Project Effort , 2007, 2007 International Joint Conference on Neural Networks.
[29] Emilia Mendes,et al. Bayesian Network Models for Web Effort Prediction: A Comparative Study , 2008, IEEE Transactions on Software Engineering.
[30] Ayse Basar Bener,et al. Ensemble of neural networks with associative memory (ENNA) for estimating software development costs , 2009, Knowl. Based Syst..
[31] Lionel C. Briand,et al. An assessment and comparison of common software cost estimation modeling techniques , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).
[32] Bart Baesens,et al. Data Mining Techniques for Software Effort Estimation: A Comparative Study , 2012, IEEE Transactions on Software Engineering.
[33] J. Scott Armstrong. The Forecasting Dictionary Updated : October 23 , 2000 , .
[34] B. Baskeles,et al. Software effort estimation using machine learning methods , 2007, 2007 22nd international symposium on computer and information sciences.
[35] Magne Jørgensen,et al. Better sure than safe? Over-confidence in judgement based software development effort prediction intervals , 2004, J. Syst. Softw..
[36] Xin Yao,et al. How to make best use of cross-company data in software effort estimation? , 2014, ICSE.
[37] P. Groenen,et al. Modern Multidimensional Scaling: Theory and Applications , 1999 .
[38] Tim Menzies,et al. How to Find Relevant Data for Effort Estimation? , 2011, 2011 International Symposium on Empirical Software Engineering and Measurement.
[39] Barry W. Boehm,et al. Bayesian Analysis of Empirical Software Engineering Cost Models , 1999, IEEE Trans. Software Eng..
[40] Bojan Cukic,et al. Predicting more from less: Synergies of learning , 2013, 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE).
[41] W. Bryc. The Normal Distribution: Characterizations with Applications , 1995 .