Machine Learning, Volume 65, 2006

Volume 65, Number 1, October 2006

• Shi Zhong:
  Semi-supervised model-based document clustering: A comparative study. 3-29
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• Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis:
  The max-min hill-climbing Bayesian network structure learning algorithm. 31-78
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• Maria-Florina Balcan, Avrim Blum, Santosh Vempala:
  Kernels as features: On kernels, margins, and low-dimensional mappings. 79-94
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• Chris Drummond, Robert C. Holte:
  Cost curves: An improved method for visualizing classifier performance. 95-130
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• Marc Boullé:
  MODL: A Bayes optimal discretization method for continuous attributes. 131-165
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• Abraham P. George, Warren B. Powell:
  Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming. 167-198
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• Y. Xiang, J. Lee:
  Learning decomposable markov networks in pseudo-independent domains with local evaluation. 199-227
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• Dougu Nam, Seunghyun Seo, Sangsoo Kim:
  An efficient top-down search algorithm for learning Boolean networks of gene expression. 229-245
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• E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao:
  An analysis of diversity measures. 247-271
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• Kar-Ann Toh:
  Training a reciprocal-sigmoid classifier by feature scaling-space. 273-308
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• Rajesh Pampapathi, Boris Mirkin, Mark Levene:
  A suffix tree approach to anti-spam email filtering. 309-338
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Volume 65, Numbers 2-3, December 2006

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