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JMLR Volume 8

Nonlinear Boosting Projections for Ensemble Construction
Nicolás García-Pedrajas, César García-Osorio, Colin Fyfe; (1):1−33, 2007.
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Multi-Task Learning for Classification with Dirichlet Process Priors
Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram; (2):35−63, 2007.
[abs][pdf][bib]

A Unified Continuous Optimization Framework for Center-Based Clustering Methods
Marc Teboulle; (3):65−102, 2007.
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Minimax Regret Classifier for Imprecise Class Distributions
Rocío Alaiz-Rodríguez, Alicia Guerrero-Curieses, Jesús Cid-Sueiro; (4):103−130, 2007.
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Distances between Data Sets Based on Summary Statistics
Nikolaj Tatti; (5):131−154, 2007.
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Building Blocks for Variational Bayesian Learning of Latent Variable Models
Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen; (6):155−201, 2007.
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A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
Sanjoy Dasgupta, Leonard Schulman; (7):203−226, 2007.
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Noise Tolerant Variants of the Perceptron Algorithm
Roni Khardon, Gabriel Wachman; (8):227−248, 2007.
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Learnability of Gaussians with Flexible Variances
Yiming Ying, Ding-Xuan Zhou; (9):249−276, 2007.
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Separating Models of Learning from Correlated and Uncorrelated Data
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan; (10):277−290, 2007.
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Comments on the “Core Vector Machines: Fast SVM Training on Very Large Data Sets"
Gaëlle Loosli, Stéphane Canu; (11):291−301, 2007.
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General Polynomial Time Decomposition Algorithms
Nikolas List, Hans Ulrich Simon; (12):303−321, 2007.
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Dynamics and Generalization Ability of LVQ Algorithms
Michael Biehl, Anarta Ghosh, Barbara Hammer; (13):323−360, 2007.
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Statistical Consistency of Kernel Canonical Correlation Analysis
Kenji Fukumizu, Francis R. Bach, Arthur Gretton; (14):361−383, 2007.
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Learning Equivariant Functions with Matrix Valued Kernels
Marco Reisert, Hans Burkhardt; (15):385−408, 2007.
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Boosted Classification Trees and Class Probability/Quantile Estimation
David Mease, Abraham J. Wyner, Andreas Buja; (16):409−439, 2007.
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Value Regularization and Fenchel Duality
Ryan M. Rifkin, Ross A. Lippert; (17):441−479, 2007.
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Integrating Naïve Bayes and FOIL
Niels Landwehr, Kristian Kersting, Luc De Raedt; (18):481−507, 2007.
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A Stochastic Algorithm for Feature Selection in Pattern Recognition
Sébastien Gadat, Laurent Younes; (19):509−547, 2007.
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Learning Horn Expressions with LOGAN-H
Marta Arias, Roni Khardon, Jérôme Maloberti; (20):549−587, 2007.
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Consistent Feature Selection for Pattern Recognition in Polynomial Time
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér; (21):589−612, 2007.
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Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
Markus Kalisch, Peter Bühlmann; (22):613−636, 2007.
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Margin Trees for High-dimensional Classification
Robert Tibshirani, Trevor Hastie; (23):637−652, 2007.
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Relational Dependency Networks
Jennifer Neville, David Jensen; (24):653−692, 2007.
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Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data
Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh; (25):693−723, 2007.
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The Pyramid Match Kernel: Efficient Learning with Sets of Features
Kristen Grauman, Trevor Darrell; (26):725−760, 2007.
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Infinitely Imbalanced Logistic Regression
Art B. Owen; (27):761−773, 2007.
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Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
Peter L. Bartlett, Ambuj Tewari; (28):775−790, 2007.
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Concave Learners for Rankboost
Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang; (29):791−812, 2007.
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Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression
Shantanu Chakrabartty, Gert Cauwenberghs; (30):813−839, 2007.
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Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
Gavin C. Cawley, Nicola L. C. Talbot; (31):841−861, 2007.
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Combining PAC-Bayesian and Generic Chaining Bounds
Jean-Yves Audibert, Olivier Bousquet; (32):863−889, 2007.
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Anytime Learning of Decision Trees
Saher Esmeir, Shaul Markovitch; (33):891−933, 2007.
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Classification in Networked Data: A Toolkit and a Univariate Case Study
Sofus A. Macskassy, Foster Provost; (34):935−983, 2007.
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Covariate Shift Adaptation by Importance Weighted Cross Validation
Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller; (35):985−1005, 2007.
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On the Consistency of Multiclass Classification Methods
Ambuj Tewari, Peter L. Bartlett; (36):1007−1025, 2007.
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Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
Masashi Sugiyama; (37):1027−1061, 2007.
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Undercomplete Blind Subspace Deconvolution
Zoltán Szabó, Barnabás Póczos, András Lőrincz; (38):1063−1095, 2007.
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Bilinear Discriminant Component Analysis
Mads Dyrholm, Christoforos Christoforou, Lucas C. Parra; (39):1097−1111, 2007.
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Loop Corrections for Approximate Inference on Factor Graphs
Joris M. Mooij, Hilbert J. Kappen; (40):1113−1143, 2007.
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Penalized Model-Based Clustering with Application to Variable Selection
Wei Pan, Xiaotong Shen; (41):1145−1164, 2007.
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Local Discriminant Wavelet Packet Coordinates for Face Recognition
Chao-Chun Liu, Dao-Qing Dai, Hong Yan; (42):1165−1195, 2007.
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Synergistic Face Detection and Pose Estimation with Energy-Based Models
Margarita Osadchy, Yann Le Cun, Matthew L. Miller; (43):1197−1215, 2007.
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Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling
Miroslav Dudík, Steven J. Phillips, Robert E. Schapire; (44):1217−1260, 2007.
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Measuring Differentiability: Unmasking Pseudonymous Authors
Moshe Koppel, Jonathan Schler, Elisheva Bonchek-Dokow; (45):1261−1276, 2007.
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Bayesian Quadratic Discriminant Analysis
Santosh Srivastava, Maya R. Gupta, Béla A. Frigyik; (46):1277−1305, 2007.
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From External to Internal Regret
Avrim Blum, Yishay Mansour; (47):1307−1324, 2007.
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Graph Laplacians and their Convergence on Random Neighborhood Graphs
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg; (48):1325−1368, 2007.
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Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
Philippe Rigollet; (49):1369−1392, 2007.
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Learning to Classify Ordinal Data: The Data Replication Method
Jaime S. Cardoso, Joaquim F. Pinto da Costa; (50):1393−1429, 2007.
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Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions
Vitaly Feldman; (51):1431−1460, 2007.
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PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers
François Laviolette, Mario Marchand; (52):1461−1487, 2007.
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On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning
Rie Johnson, Tong Zhang; (53):1489−1517, 2007.
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An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
Kwangmoo Koh, Seung-Jean Kim, Stephen Boyd; (54):1519−1555, 2007.
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Multi-class Protein Classification Using Adaptive Codes
Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie; (55):1557−1581, 2007.
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Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification
Onur C. Hamsici, Aleix M. Martinez; (56):1583−1623, 2007.
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Handling Missing Values when Applying Classification Models
Maytal Saar-Tsechansky, Foster Provost; (57):1623−1657, 2007.
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Compression-Based Averaging of Selective Naive Bayes Classifiers
Marc Boullé; (58):1659−1685, 2007.
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A Nonparametric Statistical Approach to Clustering via Mode Identification
Jia Li, Surajit Ray, Bruce G. Lindsay; (59):1687−1723, 2007.
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Polynomial Identification in the Limit of Substitutable Context-free Languages
Alexander Clark, Rémi Eyraud; (60):1725−1745, 2007.
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Structure and Majority Classes in Decision Tree Learning
Ray J. Hickey; (61):1747−1768, 2007.
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Characterizing the Function Space for Bayesian Kernel Models
Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert; (62):1769−1797, 2007.
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"Ideal Parent” Structure Learning for Continuous Variable Bayesian Networks
Gal Elidan, Iftach Nachman, Nir Friedman; (63):1799−1833, 2007.
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Behavioral Shaping for Geometric Concepts
Manu Chhabra, Robert A. Jacobs, Daniel Štefankovič; (64):1835−1865, 2007.
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Large Margin Semi-supervised Learning
Junhui Wang, Xiaotong Shen; (65):1867−1891, 2007.
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Fast Iterative Kernel Principal Component Analysis
Simon Günter, Nicol N. Schraudolph, S. V. N. Vishwanathan; (66):1893−1918, 2007.
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A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, Srujana Merugu, Dharmendra S. Modha; (67):1919−1986, 2007.
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Truncating the Loop Series Expansion for Belief Propagation
Vicenç Gómez, Joris M. Mooij, Hilbert J. Kappen; (68):1987−2016, 2007.
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Very Fast Online Learning of Highly Non Linear Problems
Aggelos Chariatis; (69):2017−2045, 2007.
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Unlabeled Compression Schemes for Maximum Classes
Dima Kuzmin, Manfred K. Warmuth; (70):2047−2081, 2007.
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Refinable Kernels
Yuesheng Xu, Haizhang Zhang; (71):2083−2120, 2007.
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A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion
Marco Loog; (72):2121−2123, 2007.
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Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
Matthew E. Taylor, Peter Stone, Yaxin Liu; (73):2125−2167, 2007.
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Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes
Sridhar Mahadevan, Mauro Maggioni; (74):2169−2231, 2007.
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Online Learning of Multiple Tasks with a Shared Loss
Ofer Dekel, Philip M. Long, Yoram Singer; (75):2233−2264, 2007.
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Euclidean Embedding of Co-occurrence Data
Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby; (76):2265−2295, 2007.
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Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization
Evgeniy Gabrilovich, Shaul Markovitch; (77):2297−2345, 2007.
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AdaBoost is Consistent
Peter L. Bartlett, Mikhail Traskin; (78):2347−2368, 2007.
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The On-Line Shortest Path Problem Under Partial Monitoring
András György, Tamás Linder, Gábor Lugosi, György Ottucsák; (79):2369−2403, 2007.
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The Locally Weighted Bag of Words Framework for Document Representation
Guy Lebanon, Yi Mao, Joshua Dillon; (80):2405−2441, 2007.
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The Need for Open Source Software in Machine Learning
Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert Williamson; (81):2443−2466, 2007.
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On the Representer Theorem and Equivalent Degrees of Freedom of SVR
Francesco Dinuzzo, Marta Neve, Giuseppe De Nicolao, Ugo Pietro Gianazza; (82):2467−2495, 2007.
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Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
Ping Li, Trevor J. Hastie, Kenneth W. Church; (83):2497−2532, 2007.
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Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data
Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, Spencer Charles Brubaker, Matthew D. Mullin; (84):2533−2549, 2007.
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VC Theory of Large Margin Multi-Category Classifiers
Yann Guermeur; (85):2551−2594, 2007.
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Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
Marlon Núñez, Raúl Fidalgo, Rafael Morales; (86):2595−2628, 2007.
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Hierarchical Average Reward Reinforcement Learning
Mohammad Ghavamzadeh, Sridhar Mahadevan; (87):2629−2669, 2007.
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Ranking the Best Instances
Stéphan Clémençon, Nicolas Vayatis; (88):2671−2699, 2007.
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Stagewise Lasso
Peng Zhao, Bin Yu; (89):2701−2726, 2007.
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A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
Carine Hue, Marc Boullé; (90):2727−2754, 2007.
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Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
J. Zico Kolter, Marcus A. Maloof; (91):2755−2790, 2007.
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© JMLR .