Description

The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region.

Related Papers

  • Thomas T. Osugi and M. S. EXPLORATION-BASED ACTIVE MACHINE LEARNING. Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements. [link]
  • C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [link]
  • Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat. [link]
  • Nikos A. Vlassis and Aristidis Likas. A greedy EM algorithm for Gaussian mixture. Intelligent Autonomous Systems, IAS. [link]
  • K. A. J Doherty and Rolf Adams and Neil Davey. Unsupervised Learning with Normalised Data and Non-Euclidean Norms. University of Hertfordshire. [link]
  • Adil M. Bagirov and John Yearwood. A new nonsmooth optimization algorithm for clustering. Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat. [link]
  • Amund Tveit. Empirical Comparison of Accuracy and Performance for the MIPSVM classifier with Existing Classifiers. Division of Intelligent Systems Department of Computer and Information Science, Norwegian University of Science and Technology. [link]
  • Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD Conference. 2005. [link]
  • Aristidis Likas and Nikos A. Vlassis and Jakob J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36. 2003. [link]
  • Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov. Selective Sampling Using Random Field Modelling. [link]
  • Je Scott and Mahesan Niranjan and Richard W. Prager. Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. Cambridge University Department of Engineering. [link]
  • K. A. J Doherty and Rolf Adams and Neil Davey. Non-Euclidean Norms and Data Normalisation. Department of Computer Science, University of Hertfordshire, College Lane. [link]
  • James Tin and Yau Kwok. Moderating the Outputs of Support Vector Machine Classifiers. Department of Computer Science Hong Kong Baptist University Hong Kong. [link]
  • Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004. [link]
  • Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Fast hierarchical clustering and its validation. Data Knowl. Eng, 44. 2003. [link]

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