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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 S. 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 - Luís 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
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