Machine Learning, Volume 65, 2006

Volume 65, Number 1, October 2006

• Shi Zhong:
  Semi-supervised model-based document clustering: A comparative study. 3-29
  
• Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis:
  The max-min hill-climbing Bayesian network structure learning algorithm. 31-78
  
• Maria-Florina Balcan, Avrim Blum, Santosh Vempala:
  Kernels as features: On kernels, margins, and low-dimensional mappings. 79-94
  
• Chris Drummond, Robert C. Holte:
  Cost curves: An improved method for visualizing classifier performance. 95-130
  
• Marc Boullé:
  MODL: A Bayes optimal discretization method for continuous attributes. 131-165
  
• Abraham P. George, Warren B. Powell:
  Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming. 167-198
  
• Y. Xiang, J. Lee:
  Learning decomposable markov networks in pseudo-independent domains with local evaluation. 199-227
  
• Dougu Nam, Seunghyun Seo, Sangsoo Kim:
  An efficient top-down search algorithm for learning Boolean networks of gene expression. 229-245
  
• E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao:
  An analysis of diversity measures. 247-271
  
• Kar-Ann Toh:
  Training a reciprocal-sigmoid classifier by feature scaling-space. 273-308
  
• Rajesh Pampapathi, Boris Mirkin, Mark Levene:
  A suffix tree approach to anti-spam email filtering. 309-338
  

Volume 65, Numbers 2-3, December 2006

• Gerhard Widmer:
  Guest editorial: Machine learning in and for music. 343-346
  
• Gerhard Widmer:
  Guest Editorial: Machine learning in and for music. 347
  
• Darrell Conklin:
  Melodic analysis with segment classes. 349-360
  
• Graham Grindlay, David P. Helmbold:
  Modeling, analyzing, and synthesizing expressive piano performance with graphical models. 361-387
  
• Christopher Raphael:
  Aligning music audio with symbolic scores using a hybrid graphical model. 389-409
  
• Maarten Grachten, Josep Lluís Arcos, Ramon López de Mántaras:
  A case based approach to expressivity-aware tempo transformation. 411-437
  
• Daniel P. W. Ellis, Graham E. Poliner:
  Classification-based melody transcription. 439-456
  
• Ning Hu, Roger B. Dannenberg:
  Bootstrap learning for accurate onset detection. 457-471
  
• James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl:
  Aggregate features and ADABOOSTfor music classification. 473-484
  
• Samer A. Abdallah, Mark B. Sandler, Christophe Rhodes, Michael Casey:
  Using duration models to reduce fragmentation in audio segmentation. 485-515