14.5.8 Learning -- Conference Listing

AAAI Fall Symposium on Machine Learning in Computer Vision,
AAAI-MLCV93Fall 1993. TR FSS-93-04. Some filed elsewhere. BibRef 9300

• Sengupta, K.[Kuntal], Boyer, K.L.[Kim L.],
  Incremental Modelbase Updating: Learning New Model Sites,
  The Ohio State University.
• Bhandaru, M.[Malini], Draper, B.A.[Bruce A.], and Lesser, V.[Victor],
  Learning Image to Symbol Conversion,
  University of Massachusetts at Amherst.
• Conklin, D.[Darrell],
  Transformation-invariant Indexing and Machine Discovery for Computer Vision,
  Queen's University.
• Dey, L., Das, P.P., and Chaudhury, S.,
  Recognition and Learning of Unknown Objects in a Hierarchical Knowledge-base,
  I.I.T., Delhi.
• Williams, C.K.I., Zemel, R.S., and Mozer, M.C.,
  Unsupervised Learning of Object Models,
  Univ. of Toronto; Univ. of Colorado.
• Murase, H.[Hiroshi], and Nayar, S.K.[Shree K.],
  Learning and Recognition of 3-D Objects from Brightness Images,
  Columbia University.
• Remagnino, P., Bober, M., and Kittler, J.V.,
  Learning About A Scene Using an Active Vision System,
  University of Surrey, UK.
• Mann, R.[Richard], and Jepson, A.D.[Allan D.],
  Non-accidental Features in Learning,
  University of Toronto.
• Viola, P.A.[Paul A.],
  Feature-Based Recognition of Objects,
  Massachusetts Institute of Technology.
• Matsubara, H.[Hitoshi], Sakaue, K.[Katsuhiko], and Yamamoto, K.[Kazuhiko],
  Learning Correspondences Between Visual Features and Functional Features,
  ETL, Japan.
• Marshall, J.A.[Johnathon A.], and Alley, R.K.[Richard K.],
  A Self-Organizing Neural Network that Learns to Detect and Represent Visual Depth from Occlusion Events,
  University of North Carolina.
• Lammens, J.M., and Shapiro, S.C.,
  Learning Symbolic Names for Perceived Colors,
  SUNY Buffalo.
• Chachere, L.[Lawrence], and Pun, T.[Thierry],
  Extracting a Domain Theory from Natural Language to Construct a Knowledge Base for Visual Recognition,
  University of Geneva.
• Salganicoff, M.[Marcos], ; Metta, G.[Giorgio], Oddera, A.[Andrea], and Sandini, G.[Giulio],
  A Vision-Based Learning Method for Pushing Manipulation,
  Univ. of Pennsylvania; University of Genoa.
• Skolnick, M.M.[Michael M.],
  A Classifier System for Learning Spatial Representations Based on a Morphological Wave Propagation Algorithm,
  R.P.I.
• Nguyen, T.C.[Thang C.], Goldberg, D.E.[David E.], and Huang, T.S.[Thomas S.],
  Evolvable Modeling: Structural Adaptation Through Hierarchical Evolution for 3-D Model-based Vision,
  University of Illinois.
• Zemel, R.S.[Richard S.], Hinton, G.E.[Geoffrey E.],
  Developing Population Codes for Object Instantiation Parameters,
  University of Toronto.
• Pachowicz, P.W.[Peter W.],
  Integration of Machine Learning and Vision into an Active Agent Paradigm,
  George Mason University.
• Wolff, G.J., Prasad, K.V., Stork, D.G., Hennecke, M.E.,
  Learning Visual Speech,
  Ricoh California Research Center.
• Schneider, J.[Jeff],
  Learning open loop control of complex motor tasks,
  University of Rochester.
• Bala, J.W., Pachowicz, P.W.,
  Issues in Learning from Noisy Sensory Data,
  George Mason University.
• Murphy, R.R.[Robin R.],
  Learning to Eliminate Background Effects in Object Recognition,
  Colorado School of Mines.
• Davidsson, P.[Paul],
  Toward a General Solution to the Symbol Grounding Problem: Combining Learning and Computer Vision,
  Lund University.
• Honavar, V.[Vasant],
  Symbolic and Subsymbolic Learning for Vision: Some Possibilities,
  Iowa State University.