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• 
  Marina Meila, Michael I. Jordan:
  Learning with Mixtures of Trees. 1-48
• 
  David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. 49-75
• 
  Justin A. Boyan, Andrew W. Moore:
  Learning Evaluation Functions to Improve Optimization by Local Search. 77-112
• 
  Erin L. Allwein, Robert E. Schapire, Yoram Singer:
  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. 113-141

Kernel Machines Section

• 
  Ronan Collobert, Samy Bengio:
  SVMTorch: Support Vector Machines for Large-Scale Regression Problems. 143-160
• 
  Olvi L. Mangasarian, David R. Musicant:
  Lagrangian Support Vector Machines. 161-177
• 
  Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
  Regularized Principal Manifolds. 179-209
• 
  Michael E. Tipping:
  Sparse Bayesian Learning and the Relevance Vector Machine. 211-244
• 
  Ralf Herbrich, Thore Graepel, Colin Campbell:
  Bayes Point Machines. 245-279
• 
  Mark Herbster, Manfred K. Warmuth:
  Tracking the Best Linear Predictor. 281-309
• 
  Robert E. Mahony, Robert C. Williamson:
  Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. 311-355

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