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JMLR Volume 10

Exploring Strategies for Training Deep Neural Networks
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin; (1):1−40, 2009.
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Markov Properties for Linear Causal Models with Correlated Errors
Changsung Kang, Jin Tian; (2):41−70, 2009.
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An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs
M. Pawan Kumar, Vladimir Kolmogorov, Philip H.S. Torr; (3):71−106, 2009.
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Refinement of Reproducing Kernels
Yuesheng Xu, Haizhang Zhang; (4):107−140, 2009.
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Subgroup Analysis via Recursive Partitioning
Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li; (5):141−158, 2009.
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Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
Abhik Shah, Peter Woolf; (6):159−162, 2009.
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On The Power of Membership Queries in Agnostic Learning
Vitaly Feldman; (7):163−182, 2009.
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Using Local Dependencies within Batches to Improve Large Margin Classifiers
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, Bharat Rao; (8):183−206, 2009.
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
Kilian Q. Weinberger, Lawrence K. Saul; (9):207−244, 2009.
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Data-driven Calibration of Penalties for Least-Squares Regression
Sylvain Arlot, Pascal Massart; (10):245−279, 2009.
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Analysis of Perceptron-Based Active Learning
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni; (11):281−299, 2009.
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Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation
Facundo Bromberg, Dimitris Margaritis; (12):301−340, 2009.
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Low-Rank Kernel Learning with Bregman Matrix Divergences
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon; (13):341−376, 2009.
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Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
Petra Kralj Novak, Nada Lavrač, Geoffrey I. Webb; (14):377−403, 2009.
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Particle Swarm Model Selection
Hugo Jair Escalante, Manuel Montes, Luis Enrique Sucar; (15):405−440, 2009.
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Generalization Bounds for Ranking Algorithms via Algorithmic Stability
Shivani Agarwal, Partha Niyogi; (16):441−474, 2009.
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Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm
Junning Li, Z. Jane Wang; (17):475−514, 2009.
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Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques
Barnabás Póczos, András Loőrincz; (18):515−554, 2009.
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On the Consistency of Feature Selection using Greedy Least Squares Regression
Tong Zhang; (19):555−568, 2009.
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Online Learning with Sample Path Constraints
Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu; (20):569−590, 2009.
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NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Pradip Ghanty, Samrat Paul, Nikhil R. Pal; (21):591−622, 2009.
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Scalable Collaborative Filtering Approaches for Large Recommender Systems
Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk; (22):623−656, 2009.
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Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions
Sébastien Bubeck, Ulrike von Luxburg; (23):657−698, 2009.
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Properties of Monotonic Effects on Directed Acyclic Graphs
Tyler J. VanderWeele, James M. Robins; (24):699−718, 2009.
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On Efficient Large Margin Semisupervised Learning: Method and Theory
Junhui Wang, Xiaotong Shen, Wei Pan; (25):719−742, 2009.
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Nieme: Large-Scale Energy-Based Models
Francis Maes; (26):743−746, 2009.
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Similarity-based Classification: Concepts and Algorithms
Yihua Chen, Eric K. Garcia, Maya R. Gupta, Ali Rahimi, Luca Cazzanti; (27):747−776, 2009.
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Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang; (28):777−801, 2009.
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A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Jacob Abernethy, Francis Bach, Theodoros Evgeniou, Jean-Philippe Vert; (29):803−826, 2009.
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Consistency and Localizability
Alon Zakai, Ya'acov Ritov; (30):827−856, 2009.
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Stable and Efficient Gaussian Process Calculations
Leslie Foster, Alex Waagen, Nabeela Aijaz, Michael Hurley, Apolonio Luis, Joel Rinsky, Chandrika Satyavolu, Michael J. Way, Paul Gazis, Ashok Srivastava; (31):857−882, 2009.
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Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
Holger Höfling, Robert Tibshirani; (32):883−906, 2009.
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Polynomial-Delay Enumeration of Monotonic Graph Classes
Jan Ramon, Siegfried Nijssen; (33):907−929, 2009.
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Java-ML: A Machine Learning Library
Thomas Abeel, Yves Van de Peer, Yvan Saeys; (34):931−934, 2009.
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Nonextensive Information Theoretic Kernels on Measures
André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo; (35):935−975, 2009.
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On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality
Wenxin Jiang; (36):977−996, 2009.
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Fourier Theoretic Probabilistic Inference over Permutations
Jonathan Huang, Carlos Guestrin, Leonidas Guibas; (37):997−1070, 2009.
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An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity
Jose M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér; (38):1071−1094, 2009.
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Universal Kernel-Based Learning with Applications to Regular Languages
Leonid (Aryeh) Kontorovich, Boaz Nadler; (39):1095−1129, 2009.
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Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Hui Li, Xuejun Liao, Lawrence Carin; (40):1131−1186, 2009.
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The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
Ricardo Silva, Zoubin Ghahramani; (41):1187−1238, 2009.
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Incorporating Functional Knowledge in Neural Networks
Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, René Garcia; (42):1239−1262, 2009.
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Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther, Manfred Opper; (43):1263−1304, 2009.
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Robust Process Discovery with Artificial Negative Events
Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens; (44):1305−1340, 2009.
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Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Eugene Tuv, Alexander Borisov, George Runger, Kari Torkkola; (45):1341−1366, 2009.
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A Parameter-Free Classification Method for Large Scale Learning
Marc Boullé; (46):1367−1385, 2009.
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Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
Troy Raeder, Nitesh V. Chawla; (47):1387−1390, 2009.
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A Least-squares Approach to Direct Importance Estimation
Takafumi Kanamori, Shohei Hido, Masashi Sugiyama; (48):1391−1445, 2009.
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Classification with Gaussians and Convex Loss
Dao-Hong Xiang, Ding-Xuan Zhou; (49):1447−1468, 2009.
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Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Jean Hausser, Korbinian Strimmer; (50):1469−1484, 2009.
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Robustness and Regularization of Support Vector Machines
Huan Xu, Constantine Caramanis, Shie Mannor; (51):1485−1510, 2009.
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Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras; (52):1511−1526, 2009.
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Bayesian Network Structure Learning by Recursive Autonomy Identification
Raanan Yehezkel, Boaz Lerner; (53):1527−1570, 2009.
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Learning Linear Ranking Functions for Beam Search with Application to Planning
Yuehua Xu, Alan Fern, Sungwook Yoon; (54):1571−1610, 2009.
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Marginal Likelihood Integrals for Mixtures of Independence Models
Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu; (55):1611−1631, 2009.
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Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew E. Taylor, Peter Stone; (56):1633−1685, 2009.
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Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Eitan Greenshtein, Junyong Park; (57):1687−1704, 2009.
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Learning Permutations with Exponential Weights
David P. Helmbold, Manfred K. Warmuth; (58):1705−1736, 2009.
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SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
Antoine Bordes, Léon Bottou, Patrick Gallinari; (59):1737−1754, 2009.
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Dlib-ml: A Machine Learning Toolkit
Davis E. King; (60):1755−1758, 2009.
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Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning
Halbert White, Karim Chalak; (61):1759−1799, 2009.
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Distributed Algorithms for Topic Models
David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling; (62):1801−1828, 2009.
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Nonlinear Models Using Dirichlet Process Mixtures
Babak Shahbaba, Radford Neal; (63):1829−1850, 2009.
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CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
Roberto Esposito, Daniele P. Radicioni; (64):1851−1880, 2009.
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Learning Acyclic Probabilistic Circuits Using Test Paths
Dana Angluin, James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin; (65):1881−1911, 2009.
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Learning Approximate Sequential Patterns for Classification
Zeeshan Syed, Piotr Indyk, John Guttag; (66):1913−1936, 2009.
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Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
Kristian Woodsend, Jacek Gondzio; (67):1937−1953, 2009.
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Provably Efficient Learning with Typed Parametric Models
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy; (68):1955−1988, 2009.
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Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Jie Chen, Haw-ren Fang, Yousef Saad; (69):1989−2012, 2009.
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Ultrahigh Dimensional Feature Selection: Beyond The Linear Model
Jianqing Fan, Richard Samworth, Yichao Wu; (70):2013−2038, 2009.
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Evolutionary Model Type Selection for Global Surrogate Modeling
Dirk Gorissen, Tom Dhaene, Filip De Turck; (71):2039−2078, 2009.
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An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
Luciana Ferrer, Kemal Sönmez, Elizabeth Shriberg; (72):2079−2114, 2009.
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Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods
Christian Rieger, Barbara Zwicknagl; (73):2115−2132, 2009.
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RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
Brian Tanner, Adam White; (74):2133−2136, 2009.
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Discriminative Learning Under Covariate Shift
Steffen Bickel, Michael Brückner, Tobias Scheffer; (75):2137−2155, 2009.
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Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
Vojtěch Franc, Sören Sonnenburg; (76):2157−2192, 2009.
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Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Cynthia Rudin, Robert E. Schapire; (77):2193−2232, 2009.
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The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
Cynthia Rudin; (78):2233−2271, 2009.
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Learning Nondeterministic Classifiers
Juan José del Coz, Jorge Díez, Antonio Bahamonde; (79):2273−2293, 2009.
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The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Han Liu, John Lafferty, Larry Wasserman; (80):2295−2328, 2009.
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Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
Mathias Drton, Michael Eichler, Thomas S. Richardson; (81):2329−2348, 2009.
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Estimating Labels from Label Proportions
Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le; (82):2349−2374, 2009.
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Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions
Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page; (83):2375−2411, 2009.
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Reinforcement Learning in Finite MDPs: PAC Analysis
Alexander L. Strehl, Lihong Li, Michael L. Littman; (84):2413−2444, 2009.
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Prediction With Expert Advice For The Brier Game
Vladimir Vovk, Fedor Zhdanov; (85):2445−2471, 2009.
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Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
Saharon Rosset; (86):2473−2505, 2009.
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When Is There a Representer Theorem? Vector Versus Matrix Regularizers
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil; (87):2507−2529, 2009.
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Maximum Entropy Discrimination Markov Networks
Jun Zhu, Eric P. Xing; (88):2531−2569, 2009.
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Learning When Concepts Abound
Omid Madani, Michael Connor, Wiley Greiner; (89):2571−2613, 2009.
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Hash Kernels for Structured Data
Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, S.V.N. Vishwanathan; (90):2615−2637, 2009.
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DL-Learner: Learning Concepts in Description Logics
Jens Lehmann; (91):2639−2642, 2009.
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Bounded Kernel-Based Online Learning
Francesco Orabona, Joseph Keshet, Barbara Caputo; (92):2643−2666, 2009.
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Structure Spaces
Brijnesh J. Jain, Klaus Obermayer; (93):2667−2714, 2009.
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Learning Halfspaces with Malicious Noise
Adam R. Klivans, Philip M. Long, Rocco A. Servedio; (94):2715−2740, 2009.
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Reproducing Kernel Banach Spaces for Machine Learning
Haizhang Zhang, Yuesheng Xu, Jun Zhang; (95):2741−2775, 2009.
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Cautious Collective Classification
Luke K. McDowell, Kalyan Moy Gupta, David W. Aha; (96):2777−2836, 2009.
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Adaptive False Discovery Rate Control under Independence and Dependence
Gilles Blanchard, Étienne Roquain; (97):2837−2871, 2009.
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Online Learning with Samples Drawn from Non-identical Distributions
Ting Hu, Ding-Xuan Zhou; (98):2873−2898, 2009.
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Efficient Online and Batch Learning Using Forward Backward Splitting
John Duchi, Yoram Singer; (99):2899−2934, 2009.
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A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
Asela Gunawardana, Guy Shani; (100):2935−2962, 2009.
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