Papers

• Multiple-Instance Kernels (ICML 2002)
  Thomas Gartner (thomas.gaertner@ais.fraunhofer.de), Peter A. Flach (peter.flach@bristol.ac.uk), Adam Kowalczyk, Alex Smola
  www-gartner pdf
• Multiple-Instance Learning of Real-Valued Data (2001)
  Robert A. Amar, Daniel R. Dooly, Sally A. Goldman, Qi Zhang
  citeseer
• EM-DD: An Improved Multiple-Instance Learning Technique (preliminary version) (2001)
  Qi Zhang, Sally A. Goldman
  citeseer
• A Framework for Learning Rules from Multiple Instance Data (2001)
  Yann Chevaleyre, Jean-Daniel Zucker (12th European Conference on Machine Learning)
  citeseer
• Multi Instance Neural Networks (2000)
  Jan Ramon, Luc De Raedt
  citeseer
• Learning single and multiple instance decision tree for computer security applications (2000)
  Ruffo, G. (Doctoral dissertation, Department of Computer Science, University of Turin, Torino, Italy)
• Solving the Multiple-Instance Problem: A Lazy Learning Approach (2000)
  Jun Wang (Proc. 17th International Conf. on Machine Learning)
  citeseer
• Multiple-Instance Learning for Natural Scene Classification (1998)
  Oded Maron, Aparna Lakshmi Ratan
  citeseer
• A Framework for Multiple-Instance Learning (1998)
  Oded Maron, Tomás Lozano-Pérez (Advances in Neural Information Processing Systems)
  ftp.ps.gz citeseer
• Top-Down Induction Of First Order Logical Decision Trees (1998)
  Hendrik Blockeel (Artificial Intelligence)
  citeseer
• Solving the Multiple-Instance Problem with Axis-Parallel Rectangles (1997)
  Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez (Artificial Intelligence)
  ftp.ps.gz citeseer
• A Training Algorithm for Optimal Margin Classifiers (1992)
  Bernhard E. Boser, Isabelle M. Guyon, Vladimir N. Vapnik
  citeseer
• Integrated Segmentation and Recognition of Hand-Printed Numerals (1991)
  James D. Keeler, David E. Rumelhart, Wee-Kheng Leow
  djvu
• Title (conf,journal/year)
  authors
  citeseer/link

People and Links

• MIL web page maintained by Oded Maron
• UCI Machine Learning Repository (musk data set)
• Thomas G. Dietterich
  tgd@cs.orst.edu
  Department of Computer Science, Oregon State University, Corvallis, OR 97331
• Oded Maron
  oded@ai.mit.edu
  AI Lab, M.I.T. Cambridge, MA 02139
• Tomas Lozano-Perez
  

Papers

• Optimization Approaches to Semi-Supervised Learning (2000)
  Ayhan Demiriz and Kristen P. Bennett
  citeseer
• Semi-Supervised Support Vector Machines (1998)
  Kristin P. Bennett, Ayhan Demiriz
  citeseer
• Transductive Inference for Text Classification using Support Vector Machines (1999)
  Thorsten Joachims
  citeseer
  
• Making Large-Scale SVM Learning Practical (1998)
  
  Thorsten Joachims
  citeseer
• Support Vector Method for Novelty Detection (2000)
  Bernhard Schölkopf, Robert Wiliamson, Alex Smola, John Shawe-Taylor and John Platt
  citeseer
• Discriminant-EM Algorithm with Application to Image Retrieval (2000)
  Ying Wu, Qi Tian, Thomas S. Huang
  citeseer
• Self-Supervised Learning for Object Recognition based on Kernel Discriminant-EM Algorithm (2001)
  Ying Wu, Thomas S. Huang, Kentaro Toyama
  citeseer
• Color Tracking by Transductive Learning (2000)
  Ying Wu, Thomas S. Huang
  citeseer
• Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback
  Ying Wu, Qi Tian, Thomas S. Huang
  citeseer
• Title (conf,journal/year)
  authors
  citeseer/link

People and Links

• MIL web page maintained by Oded Maron
• old SVM-light web page maintained by Thorsten Joachims
  new SVM-light web page maintained by Thorsten Joachims
• jSVM web page
• Thomas G. Dietterich
  tgd@cs.orst.edu
  Department of Computer Science, Oregon State University, Corvallis, OR 97331

Papers

• Self-Supervised Learning for Object Recognition based on Kernel Discriminant-EM Algorithm (2001)
Ying Wu, Thomas S. Huang, Kentaro Toyama
citeseer
• Kernel Independent Component Analysis (2001) 
Francis R. Bach, Michael I. Jordan
citeseer
• Covariance Kernels from Bayesian Generative Models (2001)
Matthias Seeger
citeseer
• Course on Kernels (2000)
Nello Christianinni
course notes
• Improving Support Vector Machine Classifiers by Modifying Kernel Functions (Neural Networks 1999)
Shun-Ichi Amari, Si Wu
citeseer
• Natural Regularization in SVMs (1999)
Nuria Oliver, Bernhard Schölkopf, Alex Smola
citeseer
• Generalization Bounds via Eigenvalues of the Gram Matrix (1999) 
Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson
citeseer
• Probabilistic kernel regression models (1999)
Tommi S. Jaakkola, David Haussler
citeseer
• Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators (1999) 
Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf
citeseer
• From Regularization Operators to Support Vector Kernels (1998) 
Alexander Smola, Bernhard Schölkopf
citeseer
• The Connection between Regularization Operators and Support Vector Kernels (1998)
Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller
citeseer
• General Cost Functions for Support Vector Regression (1998)
Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller
citeseer
• Exploiting generative models in discriminative classifiers (1998)
Tommi S. Jaakkola and David Haussler
citeseer
• Nonlinear component analysis as a kernel eigenvalue problem (1998) 
B. Scholkopf, A. Smola, and K.-R. Muller
citeseer
• An Equivalence Between Sparse Approximation and Support Vector Machines (1997) 
Nuria Oliver, Bernhard Schölkopf, Alex Smola
citeseer
• Similarity Metric Learning for a Variable-Kernel Classifier (1995)
David Lowe
citeseer  / html version
• White Noise As An Infinite Dimensional Calculus (1993)
T. Hida, H. Kuo, J. Potthoff, L. Streit
citeseer (citations)
• The Role of Differential Geometry in Statistical Theory (1986)
O. Barndorff-Nielsen, D. Cox, N. Reid
Barndorff-Nielsen, O. E., D. R. Cox, and N. Reid (1986). The Role of Differential Geometry in Statistical Theory, International Statistical Review, 54, 83--96.
• Title (conf,journal/year) 
authors
citeseer

People and Links

• Advanced Topics in Machine Learning (2001)
  Peter Bartlett, Nello Christianini, Michael Jordan, Stuart Russell
  www
• Machine Learning and Pattern Recognition (2001)
  Thomas Hofmann
  www
• Statistical Learning Theory and Applications (2002)
  Tomaso Poggio, Sayan Mukherjee, Ryan Rifkin
  www
• Networks for Learning: Regression and Classification (2001)
  Alessandro Verri, Tomaso Poggio
  www
• Learning, Approximation and Networks (1995)
  Tomaso Poggio, Federico Girosi
  www
• web site
  description etc.
  
• Name
  name
  email
  work affiliation

Papers

• Self-Supervised Learning for Object Recognition based on Kernel Discriminant-EM Algorithm (2001)
  Ying Wu, Thomas S. Huang, Kentaro Toyama
  citeseer
• Discriminant-EM Algorithm with Application to Image Retrieval (2000)
  Ying Wu, Qi Tian, Thomas S. Huang
  citeseer
• Color Tracking by Transductive Learning (2000)
  Ying Wu, Thomas S. Huang
  citeseer
• Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback (2000)
  Ying Wu, Qi Tian, Thomas S. Huang
  citeseer
• Learning the Semantics of Words and Pictures (2000)
  Kobus Barnard and David Forsyth
  citeseer
• Blobworld: Image segmentation using Expectation-Maximization and its application to image querying (1999)
  C. Carson, S. Belonge, H. Greenspan, and J. Malik
  citeseer
• Statistical models for co-occurrence data (1998)
  Hofmann T. and Puzicha J.
  citeseer ??
• Learning and representing topic. A hierarchical mixture model for word occurrence in document databases (1998)
  Hofmann T.
  citeseer ??
• Normalized Cuts and Image Segmentation (1997)
  Jianbo Shi and Jitendra Malik
  citeseer
• Title (conf,journal/year)
  authors
  citeseer/link
• Berkeley vision web page
• Blobworld web page
• name
  email
  work affiliation