Journal of Machine Learning Research, Volume 4

Volume 4, April 2003

• Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky:
  On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines. 1-15
  
• Vassilios Petridis, Vassilis G. Kaburlasos:
  FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples. 17-37
  
• Stefan W. Christensen, Ian Sinclair, Philippa A. S. Reed:
  Designing Committees of Models through Deliberate Weighting of Data Points. 39-66
  
• Koji Tsuda, Shotaro Akaho, Kiyoshi Asai:
  The em Algorithm for Kernel Matrix Completion with Auxiliary Data. 67-81
  

Volume 4, May 2003

• Bart Bakker, Tom Heskes:
  Task Clustering and Gating for Bayesian Multitask Learning. 83-99
  
• Dmitry Gavinsky:
  Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning. 101-117
  

Volume 4, June 2003

• Lawrence K. Saul, Sam T. Roweis:
  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold. 119-155
  
• Nader H. Bshouty, Lynn Burroughs:
  On the Proper Learning of Axis-Parallel Concepts. 157-176
  
• Mary Elaine Califf, Raymond J. Mooney:
  Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction. 177-210
  
• Claudia Perlich, Foster J. Provost, Jeffrey S. Simonoff:
  Tree Induction vs. Logistic Regression: A Learning-Curve Analysis. 211-255
  

Volume 4, July 2003

• Craig Friedman, Sven Sandow:
  Learning Probabilistic Models: An Expected Utility Maximization Approach. 257-291
  

• Marek J. Druzdzel, Francisco Javier Díez:
  Combining Knowledge from Different Sources in Causal Probabilistic Models. 295-316
  
• Peter Haddawy, Vu A. Ha, Angelo C. Restificar, Benjamin Geisler, John Miyamoto:
  Preference Elicitation via Theory Refinement. 317-337
  
• Helge Langseth, Thomas D. Nielsen:
  Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. 339-368
  
• Ashwin Srinivasan, Ross D. King, Michael Bain:
  An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. 369-383
  

• Sandra Clara Gadanho:
  Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task. 385-412
  

Volume 4, August 2003

• David Page, Ashwin Srinivasan:
  ILP: A Short Look Back and a Longer Look Forward. 415-430
  
• Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag:
  Relational Learning as Search in a Critical Region. 431-463
  
• Vítor Santos Costa, Ashwin Srinivasan, Rui Camacho, Hendrik Blockeel, Bart Demoen, Gerda Janssens, Jan Struyf, Henk Vandecasteele, Wim Van Laer:
  Query Transformations for Improving the Efficiency of ILP Systems. 465-491
  
• Vincent Claveau, Pascale Sébillot, Cécile Fabre, Pierrette Bouillon:
  Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming. 493-525
  

Volume 4, September 2003

• Robert Castelo, Tomás Kocka:
  On Inclusion-Driven Learning of Bayesian Networks. 527-574
  
• Pierre Baldi, Gianluca Pollastri:
  The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem. 575-602
  
• Orlando Cicchello, Stefan C. Kremer:
  Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results. 603-632
  
• Rocco A. Servedio:
  Smooth Boosting and Learning with Malicious Noise. 633-648
  
• Shaul Markovitch, Asaf Shatil:
  Speedup Learning for Repair-based Search by Identifying Redundant Steps. 649-682
  

Volume 4, October 2003

• Bertrand Clarke:
  Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored. 683-712
  
• Shie Mannor, Ron Meir, Tong Zhang:
  Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. 713-741
  
• Christian d'Avignon, Donald Geman:
  Tree-Structured Neural Decoding. 743-754
  

• Ralf Herbrich, Thore Graepel:
  Introduction to the Special Issue on Learning Theory. 755-757
  
• Shahar Mendelson:
  On the Performance of Kernel Classes. 759-771
  
• Eiji Takimoto, Manfred K. Warmuth:
  Path Kernels and Multiplicative Updates. 773-818
  
• Chris Mesterharm:
  Tracking Linear-threshold Concepts with Winnow. 819-838
  
• Ron Meir, Tong Zhang:
  Generalization Error Bounds for Bayesian Mixture Algorithms. 839-860
  
• Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis:
  On the Rate of Convergence of Regularized Boosting Classifiers. 861-894
  
• David A. McAllester, Luis E. Ortiz:
  Concentration Inequalities for the Missing Mass and for Histogram Rule Error. 895-911
  

• Lior Wolf, Amnon Shashua:
  Learning over Sets using Kernel Principal Angles. 913-931
  

Volume 4, November 2003

• Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer:
  An Efficient Boosting Algorithm for Combining Preferences. 933-969
  
• Marcus Hutter:
  Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet. 971-1000
  
• Shi Zhong, Joydeep Ghosh:
  A Unified Framework for Model-based Clustering. 1001-1037
  
• Junling Hu, Michael P. Wellman:
  Nash Q-Learning for General-Sum Stochastic Games. 1039-1069
  
• Ingo Steinwart:
  Sparseness of Support Vector Machines. 1071-1105
  

Volume 4, December 2003

• Michail G. Lagoudakis, Ronald Parr:
  Least-Squares Policy Iteration. 1107-1149
  
• Dörthe Malzahn, Manfred Opper:
  An Approximate Analytical Approach to Resampling Averages. 1151-1173
  

• Te-Won Lee, Jean-François Cardoso, Erkki Oja, Shun-ichi Amari:
  Introduction to Special Issue on Independent Components Analysis. 1175-1176
  
• Jean-François Cardoso:
  Dependence, Correlation and Gaussianity in Independent Component Analysis. 1177-1203
  
• Francis R. Bach, Michael I. Jordan:
  Beyond Independent Components: Trees and Clusters. 1205-1233
  
• Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton:
  Energy-Based Models for Sparse Overcomplete Representations. 1235-1260
  
• Lucas C. Parra, Paul Sajda:
  Blind Source Separation via Generalized Eigenvalue Decomposition. 1261-1269
  
• Erik G. Learned-Miller, John W. Fisher III:
  ICA Using Spacings Estimates of Entropy. 1271-1295
  
• Luis B. Almeida:
  MISEP -- Linear and Nonlinear ICA Based on Mutual Information. 1297-1318
  
• Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling, Klaus-Robert Müller:
  Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation. 1319-1338
  
• Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi:
  A Multiscale Framework For Blind Separation of Linearly Mixed Signals. 1339-1363
  
• Gil-Jin Jang, Te-Won Lee:
  A Maximum Likelihood Approach to Single-channel Source Separation. 1365-1392
  
• Gleb Basalyga, Magnus Rattray:
  Statistical Dynamics of On-line Independent Component Analysis. 1393-1410
  
• Khurram Waheed, Fathi M. Salem:
  Blind Source Recovery: A Framework in the State Space. 1411-1446
  
• Jaakko Särelä, Ricardo Vigário:
  Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions. 1447-1469
  
• Stéphane Bounkong, Borémi Toch, David Saad, David Lowe:
  ICA for Watermarking Digital Images. 1471-1498
  
• Inna Stainvas, David Lowe:
  A Generative Model for Separating Illumination and Reflectance from Images. 1499-1519