Conformal and Probabilistic Prediction with Applications
暂无分享,去创建一个
[1] A. Gammerman,et al. Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors , 2008, Statistical applications in genetics and molecular biology.
[2] Lior Wolf,et al. Learning over Sets using Kernel Principal Angles , 2003, J. Mach. Learn. Res..
[3] Xin Liu,et al. A Comparison of Three Implementations of Multi-Label Conformal Prediction , 2015, SLDS.
[4] Harris Papadopoulos,et al. A Cross-Conformal Predictor for Multi-label Classification , 2014, AIAI Workshops.
[5] Harris Papadopoulos. Qualified Predictions for Large Data Sets , 2009, Knowl. Eng. Rev..
[6] Stéphane Grihon,et al. Surrogate Modeling of Stability Constraints for Optimization of Composite Structures , 2013 .
[7] Radford M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.
[8] Alexander Gammerman,et al. Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines , 2005, Int. J. Neural Syst..
[9] Gene H. Golub,et al. Matrix computations , 1983 .
[10] David Barber,et al. Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[12] Harris Papadopoulos,et al. Regression Conformal Prediction with Nearest Neighbours , 2014, J. Artif. Intell. Res..
[13] Vladimir Vovk. Conformal Prediction for Reliable Machine Learning , 2014 .
[14] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[15] Harris Papadopoulos,et al. Reliable diagnosis of acute abdominal pain with conformal prediction , 2009 .
[16] D. Higdon,et al. Computer Model Calibration Using High-Dimensional Output , 2008 .
[17] Pascal Frossard,et al. Tangent space estimation for smooth embeddings of Riemannian manifolds , 2012 .
[18] Shiliang Sun,et al. A review of Nyström methods for large-scale machine learning , 2015, Inf. Fusion.
[19] A. Singer,et al. Vector diffusion maps and the connection Laplacian , 2011, Communications on pure and applied mathematics.
[20] Daniel D. Lee,et al. Grassmann discriminant analysis: a unifying view on subspace-based learning , 2008, ICML '08.
[21] Andrey Pepelyshev,et al. The Role of the Nugget Term in the Gaussian Process Method , 2010, 1005.4385.
[22] Jeong‐Soo Park. Optimal Latin-hypercube designs for computer experiments , 1994 .
[23] Ilia Nouretdinov,et al. Prediction with Confidence Based on a Random Forest Classifier , 2010, AIAI.
[24] Tom Dhaene,et al. Efficient Multi-Objective Simulation-Driven Antenna Design Using Co-Kriging , 2014, IEEE Transactions on Antennas and Propagation.
[25] Sunho Park,et al. Hierarchical Gaussian Process Regression , 2010, ACML.
[26] Daniel N. Kaslovsky,et al. Non-Asymptotic Analysis of Tangent Space Perturbation , 2011 .
[27] W. Gasarch,et al. The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .
[28] Alexander Gammerman,et al. Learning by Transduction , 1998, UAI.
[29] Carolyn Conner Seepersad,et al. Building Surrogate Models Based on Detailed and Approximate Simulations , 2004, DAC 2004.
[30] L. Wasserman. All of Nonparametric Statistics , 2005 .
[31] Jian Chen,et al. Optimal design of aeroengine turbine disc based on kriging surrogate models , 2011 .
[32] Alexander Gammerman,et al. Transduction with Confidence and Credibility , 1999, IJCAI.
[33] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[34] Qing Li,et al. A two-stage multi-fidelity optimization procedure for honeycomb-type cellular materials , 2010 .
[35] Grigorios Tsoumakas,et al. Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..
[36] Fan Yang,et al. Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine , 2014, PloS one.
[37] Dipak K. Maiti,et al. Structural Optimization of Rotating Disk Using Response Surface Equation and Genetic Algorithm , 2013 .
[38] David Ruppert,et al. Tapered Covariance: Bayesian Estimation and Asymptotics , 2012 .
[39] Mikael A. Langthjem,et al. Multifidelity Response Surface Approximations for the Optimum Design of Diffuser Flows , 2001 .
[40] Alexander Gammerman,et al. Feature Selection by Conformal Predictor , 2011, EANN/AIAI.
[41] Roderick Murray-Smith,et al. Hierarchical Gaussian process mixtures for regression , 2005, Stat. Comput..
[42] Harris Papadopoulos,et al. Confidence Predictions for the Diagnosis of Acute Abdominal Pain , 2009, AIAI.
[43] Zhenghong Gao,et al. Variable Fidelity Surrogate Assisted Optimization Using A Suite of Low Fidelity Solvers , 2012 .
[44] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .
[45] Stefan Görtz,et al. Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function , 2013 .
[46] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[47] Piotr Synak,et al. Multi-Label Classification of Emotions in Music , 2006, Intelligent Information Systems.
[48] R. M. Srivastava,et al. Integrating Seismic Data in Reservoir Modeling: The Collocated Cokriging Alternative , 1992 .
[49] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[50] Sethuraman Panchanathan,et al. Conformal predictions for information fusion , 2014, Annals of Mathematics and Artificial Intelligence.