Machine Learning, Volume 21, 1995

Volume 21, Numbers 1-2, 1995

• Jude W. Shavlik, Lawrence Hunter, David B. Searls:
  Introduction. 5-9
  
• Rebecca J. Parsons, Stephanie Forrest, Christian Burks:
  Genetic Algorithms, Operators, and DNA Fragment Assembly. 11-33
  
• Aleksandar Milosavljevic:
  Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons. 35-50
  
• Timothy L. Bailey, Charles Elkan:
  Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. 51-80
  
• Dawn M. Cohen, Casimir A. Kulikowski, Helen Berman:
  Dexter: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration. 81-101
  
• Alan S. Lapedes, Evan W. Steeg, Robert M. Farber:
  Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes. 103-124
  
• Darrell Conklin:
  Machine Discovery of Protein Motifs. 125-150
  
• Thomas R. Ioerger, Larry A. Rendell, Shankar Subramaniam:
  Searching for Representations to Improve Protein Sequence Fold-Class Prediction. 151-175
  
• Cathy H. Wu, Michael W. Berry, Sailaja Shivakumar:
  Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition. 177-193
  

Volume 21, Number 3, 1995

• Andrew W. Moore, Christopher G. Atkeson:
  The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces. 199-233
  
• Jianping Zhang, Ryszard S. Michalski:
  An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts. 235-267
  
• Sally Floyd, Manfred K. Warmuth:
  Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. 269-304
  

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