Delve Datasets

Delve Datasets: Collections of data for developing, evaluating, and comparing learning methods. The Delve datasets and families are available from this page. Every dataset (or family) has a brief overview page and many also have detailed documentation. You can download gzipped-tar files of the datasets, but you will require the delve software environment to get maximum benefit from them. Datasets are categorized as primarily assessment, development or historical according to their recommended use. Within each category we have distinguished datasets as regression or classification according to how their prototasks have been created. Details on how to install the downloaded datasets are given below .

References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial: Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (2016)
  2. Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen: Fast Gaussian kernel learning for classification tasks based on specially structured global optimization (2014)
  3. Huang, Rongqing; Sun, Shiliang: Kernel regression with sparse metric learning (2013)
  4. Vehtari, Aki; Ojanen, Janne: A survey of Bayesian predictive methods for model assessment, selection and comparison (2012)
  5. Hoffmann, Heiko; Schaal, Stefan; Vijayakumar, Sethu: Local dimensionality reduction for non-parametric regression (2009)
  6. Siermala, Markku; Juhola, Martti; Laurikkala, Jorma; Iltanen, Kati; Kentala, Erna; Pyykkö, Ilmari: Evaluation and classification of otoneurological data with new data analysis methods based on machine learning (2007)
  7. Weston, Jason; Elisseeff, André; Schölkopf, Bernhard; Tipping, Mike: Use of the zero-norm with linear models and kernel methods (2003)
  8. Baesens, Bart; Viaene, Stijn; Van den Poel, Dirk; Vanthienen, Jan; Dedene, Guido: Bayesian neural network learning for repeat purchase modelling in direct marketing (2002)
  9. Lampinen, Jouko; Vehtari, Aki: Bayesian approach for neural networks -- review and case studies. (2001)
  10. Vellido, A.; Lisboa, P.J.G.: An electronic commerce application of the Bayesian framework for MLPs: The effect of marginalisation and aRD (2001)
  11. Vehtari, Aki; Lampinen, Jouko: Bayesian MLP neural networks for image analysis (2000)
  12. Neal, Radford M.: Assessing relevance determination methods using DELVE (1999)