Journal of Machine Learning Research, Volume 8

Volume 8, 2007

• Nicolás García-Pedrajas, Cesar García-Osorio, Colin Fyfe:
  Nonlinear Boosting Projections for Ensemble Construction. 1-33
  
• Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram:
  Multi-Task Learning for Classification with Dirichlet Process Priors. 35-63
  
• Marc Teboulle:
  A Unified Continuous Optimization Framework for Center-Based Clustering Methods. 65-102
  
• Rocío Alaíz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-Sueiro:
  Minimax Regret Classifier for Imprecise Class Distributions. 103-130
  
• Nikolaj Tatti:
  Distances between Data Sets Based on Summary Statistics. 131-154
  
• Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen:
  Building Blocks for Variational Bayesian Learning of Latent Variable Models. 155-201
  
• Sanjoy Dasgupta, Leonard J. Schulman:
  A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians. 203-226
  
• Roni Khardon, Gabriel Wachman:
  Noise Tolerant Variants of the Perceptron Algorithm. 227-248
  
• Yiming Ying, Ding-Xuan Zhou:
  Learnability of Gaussians with Flexible Variances. 249-276
  
• Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan:
  Separating Models of Learning from Correlated and Uncorrelated Data. 277-290
  
• Gaëlle Loosli, Stéphane Canu:
  Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets". 291-301
  
• Nikolas List, Hans-Ulrich Simon:
  General Polynomial Time Decomposition Algorithms. 303-321
  
• Michael Biehl, Anarta Ghosh, Barbara Hammer:
  Dynamics and Generalization Ability of LVQ Algorithms. 323-360
  
• Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
  Statistical Consistency of Kernel Canonical Correlation Analysis. 361-383
  
• Marco Reisert, Hans Burkhardt:
  Learning Equivariant Functions with Matrix Valued Kernels. 385-408
  
• David Mease, Abraham J. Wyner, Andreas Buja:
  Boosted Classification Trees and Class Probability/Quantile Estimation. 409-439
  
• Ryan M. Rifkin, Ross A. Lippert:
  Value Regularization and Fenchel Duality. 441-479
  
• Niels Landwehr, Kristian Kersting, Luc De Raedt:
  Integrating Naïve Bayes and FOIL. 481-507
  
• Sébastien Gadat, Laurent Younes:
  A Stochastic Algorithm for Feature Selection in Pattern Recognition. 509-547
  
• Marta Arias, Roni Khardon, Jérôme Maloberti:
  Learning Horn Expressions with LOGAN-H. 549-587
  
• Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
  Consistent Feature Selection for Pattern Recognition in Polynomial Time. 589-612
  
• Markus Kalisch, Peter Bühlmann:
  Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. 613-636
  
• Robert Tibshirani, Trevor Hastie:
  Margin Trees for High-dimensional Classification. 637-652
  
• Jennifer Neville, David Jensen:
  Relational Dependency Networks. 653-692
  
• Charles A. Sutton, Andrew McCallum, Khashayar Rohanimanesh:
  Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. 693-723
  
• Kristen Grauman, Trevor Darrell:
  The Pyramid Match Kernel: Efficient Learning with Sets of Features. 725-760
  
• Art B. Owen:
  Infinitely Imbalanced Logistic Regression. 761-773
  
• Peter L. Bartlett, Ambuj Tewari:
  Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. 775-790
  
• Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang:
  Concave Learners for Rankboost. 791-812
  
• Shantanu Chakrabartty, Gert Cauwenberghs:
  Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression. 813-839
  
• Gavin C. Cawley, Nicola L. C. Talbot:
  Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters. 841-861
  
• Jean-Yves Audibert, Olivier Bousquet:
  Combining PAC-Bayesian and Generic Chaining Bounds. 863-889
  
• Saher Esmeir, Shaul Markovitch:
  Anytime Learning of Decision Trees. 891-933
  
• Sofus A. Macskassy, Foster J. Provost:
  Classification in Networked Data: A Toolkit and a Univariate Case Study. 935-983
  
• Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller:
  Covariate Shift Adaptation by Importance Weighted Cross Validation. 985-1005
  
• Ambuj Tewari, Peter L. Bartlett:
  On the Consistency of Multiclass Classification Methods. 1007-1025
  
• Masashi Sugiyama:
  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. 1027-1061
  
• Zoltán Szabó, Barnabás Póczos, András Lörincz:
  Undercomplete Blind Subspace Deconvolution. 1063-1095
  
• Mads Dyrholm, Christoforos Christoforou, Lucas C. Parra:
  Bilinear Discriminant Component Analysis. 1097-1111
  
• Joris M. Mooij, Hilbert J. Kappen:
  Loop Corrections for Approximate Inference on Factor Graphs. 1113-1143
  
• Wei Pan, Xiaotong Shen:
  Penalized Model-Based Clustering with Application to Variable Selection. 1145-1164
  
• Chao-Chun Liu, Dao-Qing Dai, Hong Yan:
  Local Discriminant Wavelet Packet Coordinates for Face Recognition. 1165-1195
  
• Margarita Osadchy, Yann LeCun, Matthew L. Miller:
  Synergistic Face Detection and Pose Estimation with Energy-Based Models. 1197-1215
  
• Miroslav Dudík, Steven J. Phillips, Robert E. Schapire:
  Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling. 1217-1260
  
• Moshe Koppel, Jonathan Schler, Elisheva Bonchek-Dokow:
  Measuring Differentiability: Unmasking Pseudonymous Authors. 1261-1276
  
• Santosh Srivastava, Maya R. Gupta, Bela A. Frigyik:
  Bayesian Quadratic Discriminant Analysis. 1277-1305
  
• Avrim Blum, Yishay Mansour:
  From External to Internal Regret. 1307-1324
  
• Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
  Graph Laplacians and their Convergence on Random Neighborhood Graphs. 1325-1368
  
• Philippe Rigollet:
  Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption. 1369-1392
  
• Jaime S. Cardoso, Joaquim F. Pinto da Costa:
  Learning to Classify Ordinal Data: The Data Replication Method. 1393-1429
  
• Vitaly Feldman:
  Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions. 1431-1460
  
• François Laviolette, Mario Marchand:
  PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. 1461-1487
  
• Rie Johnson, Tong Zhang:
  On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning. 1489-1517
  
• Kwangmoo Koh, Seung-Jean Kim, Stephen P. Boyd:
  An Interior-Point Method for Large-Scale l₁-Regularized Logistic Regression. 1519-1555
  
• Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
  Multi-class Protein Classification Using Adaptive Codes. 1557-1581
  
• Onur C. Hamsici, Aleix M. Martínez:
  Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification. 1583-1623
  
• Maytal Saar-Tsechansky, Foster J. Provost:
  Handling Missing Values when Applying Classification Models. 1623-1657
  
• Marc Boullé:
  Compression-Based Averaging of Selective Naive Bayes Classifiers. 1659-1685
  
• Jia Li, Surajit Ray, Bruce G. Lindsay:
  A Nonparametric Statistical Approach to Clustering via Mode Identification. 1687-1723
  
• Alexander Clark, Rémi Eyraud:
  Polynomial Identification in the Limit of Substitutable Context-free Languages. 1725-1745
  
• Ray J. Hickey:
  Structure and Majority Classes in Decision Tree Learning. 1747-1768
  
• Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert:
  Characterizing the Function Space for Bayesian Kernel Models. 1769-1797
  
• Gal Elidan, Iftach Nachman, Nir Friedman:
  "Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks. 1799-1833
  
• Manu Chhabra, Robert A. Jacobs, Daniel Stefankovic:
  Behavioral Shaping for Geometric Concepts. 1835-1865
  
• Junhui Wang, Xiaotong Shen:
  Large Margin Semi-supervised Learning. 1867-1891
  
• Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan:
  Fast Iterative Kernel Principal Component Analysis. 1893-1918
  
• Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha:
  A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation. 1919-1986
  
• Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen:
  Truncating the Loop Series Expansion for Belief Propagation. 1987-2016
  
• Aggelos Chariatis:
  Very Fast Online Learning of Highly Non Linear Problems. 2017-2045
  
• Dima Kuzmin, Manfred K. Warmuth:
  Unlabeled Compression Schemes for Maximum Classes. 2047-2081
  
• Yuesheng Xu, Haizhang Zhang:
  Refinable Kernels. 2083-2120
  
• Marco Loog:
  A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. 2121-2123
  
• Matthew E. Taylor, Peter Stone, Yaxin Liu:
  Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. 2125-2167
  
• Sridhar Mahadevan, Mauro Maggioni:
  Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes. 2169-2231
  
• Ofer Dekel, Philip M. Long, Yoram Singer:
  Online Learning of Multiple Tasks with a Shared Loss. 2233-2264
  
• Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
  Euclidean Embedding of Co-occurrence Data. 2265-2295
  
• Evgeniy Gabrilovich, Shaul Markovitch:
  Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization. 2297-2345
  
• Peter L. Bartlett, Mikhail Traskin:
  AdaBoost is Consistent. 2347-2368
  
• András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
  The On-Line Shortest Path Problem Under Partial Monitoring. 2369-2403
  
• Guy Lebanon, Yi Mao, Joshua V. Dillon:
  The Locally Weighted Bag of Words Framework for Document Representation. 2405-2441
  
• Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson:
  The Need for Open Source Software in Machine Learning. 2443-2466
  
• Francesco Dinuzzo, Marta Neve, Giuseppe De Nicolao, Ugo Pietro Gianazza:
  On the Representer Theorem and Equivalent Degrees of Freedom of SVR. 2467-2495
  
• Ping Li, Trevor Hastie, Kenneth Ward Church:
  Nonlinear Estimators and Tail Bounds for Dimension Reduction in l₁ Using Cauchy Random Projections. 2497-2532
  
• Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin:
  Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. 2533-2549
  
• Yann Guermeur:
  VC Theory of Large Margin Multi-Category Classifiers. 2551-2594
  
• Marlon Núñez, Raúl Fidalgo, Rafael Morales:
  Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners. 2595-2628
  
• Mohammad Ghavamzadeh, Sridhar Mahadevan:
  Hierarchical Average Reward Reinforcement Learning. 2629-2669
  
• Stéphan Clémençon, Nicolas Vayatis:
  Ranking the Best Instances. 2671-2699
  
• Peng Zhao, Bin Yu:
  Stagewise Lasso. 2701-2726
  
• Carine Hue, Marc Boullé:
  A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning. 2727-2754
  
• J. Zico Kolter, Marcus A. Maloof:
  Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. 2755-2790
  

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