Machine Learning, Volume 51, 2003

Volume 51, Number 1, April 2003

• David A. McAllester:
  PAC-Bayesian Stochastic Model Selection. 5-21
  
• Jürgen Forster, Manfred K. Warmuth:
  Relative Loss Bounds for Temporal-Difference Learning. 23-50
  
• Don R. Hush, Clint Scovel:
  Polynomial-Time Decomposition Algorithms for Support Vector Machines. 51-71
  
• Terran Lane, Carla E. Brodley:
  An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection. 73-107
  

Volume 51, Number 2, May 2003

• Gareth James:
  Variance and Bias for General Loss Functions. 115-135
  
• Dana Angluin, Martins Krikis:
  Learning from Different Teachers. 137-163
  
• John Langford, Avrim Blum:
  Microchoice Bounds and Self Bounding Learning Algorithms. 165-179
  
• Ludmila I. Kuncheva, Christopher J. Whitaker:
  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. 181-207
  

Volume 51, Number 3, June 2003

• Claudio Gentile:
  Guest Editor's Introduction. 215-216
  
• Adam R. Klivans, Rocco A. Servedio:
  Boosting and Hard-Core Set Construction. 217-238
  
• Nicolò Cesa-Bianchi, Gábor Lugosi:
  Potential-Based Algorithms in On-Line Prediction and Game Theory. 239-261
  
• Jürgen Forster, Niels Schmitt, Hans-Ulrich Simon, Thorsten Suttorp:
  Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces. 263-281
  
• Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long:
  A Theoretical Analysis of Query Selection for Collaborative Filtering. 283-298
  

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