JMLR Volume 9
- Max-margin Classification of Data with Absent Features
- Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller; (1):1−21, 2008.
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- Linear-Time Computation of Similarity Measures for Sequential Data
- Konrad Rieck, Pavel Laskov; (2):23−48, 2008.
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- On the Suitable Domain for SVM Training in Image Coding
- Gustavo Camps-Valls, Juan Gutiérrez, Gabriel Gómez-Pérez, Jesús Malo; (3):49−66, 2008.
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- Discriminative Learning of Max-Sum Classifiers
- Vojtěch Franc, Bogdan Savchynskyy; (4):67−104, 2008.
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- Active Learning by Spherical Subdivision
- Falk-Florian Henrich, Klaus Obermayer; (5):105−130, 2008.
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- Evidence Contrary to the Statistical View of Boosting
- David Mease, Abraham Wyner; (6):131−156, 2008.
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- Optimization Techniques for Semi-Supervised Support Vector Machines
- Olivier Chapelle, Vikas Sindhwani, Sathiya S. Keerthi; (7):203−233, 2008.
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- Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
- Andreas Krause, Ajit Singh, Carlos Guestrin; (8):235−284, 2008.
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- Support Vector Machinery for Infinite Ensemble Learning
- Hsuan-Tien Lin, Ling Li; (9):285−312, 2008.
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- Algorithms for Sparse Linear Classifiers in the Massive Data Setting
- Suhrid Balakrishnan, David Madigan; (10):313−337, 2008.
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- Generalization from Observed to Unobserved Features by Clustering
- Eyal Krupka, Naftali Tishby; (11):339−370, 2008.
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- A Tutorial on Conformal Prediction
- Glenn Shafer, Vladimir Vovk; (12):371−421, 2008.
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- Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
- Liviu Panait, Karl Tuyls, Sean Luke; (13):423−457, 2008.
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- A Recursive Method for Structural Learning of Directed Acyclic Graphs
- Xianchao Xie, Zhi Geng; (14):459−483, 2008.
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- Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
- Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont; (15):485−516, 2008.
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- Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion
- Jieping Ye; (16):517−519, 2008.
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- Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
- Bo Jiang, Xuegong Zhang, Tianxi Cai; (17):521−540, 2008.
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- An Information Criterion for Variable Selection in Support Vector Machines
- Gerda Claeskens, Christophe Croux, Johan Van Kerckhoven; (18):541−558, 2008.
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- Closed Sets for Labeled Data
- Gemma C. Garriga, Petra Kralj, Nada Lavrač; (19):559−580, 2008.
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- Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
- Giorgio Corani, Marco Zaffalon; (20):581−621, 2008.
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- A Library for Locally Weighted Projection Regression
- Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; (21):623−626, 2008. (Machine Learning Open Source Software Paper)
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[code]
- Trust Region Newton Method for Logistic Regression
- Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi; (22):627−650, 2008.
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- Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns
- Shann-Ching Chen, Geoffrey J. Gordon, Robert F. Murphy; (23):651−682, 2008.
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- Learning Control Knowledge for Forward Search Planning
- Sungwook Yoon, Alan Fern, Robert Givan; (24):683−718, 2008.
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- Multi-class Discriminant Kernel Learning via Convex Programming
- Jieping Ye, Shuiwang Ji, Jianhui Chen; (25):719−758, 2008.
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- Bayesian Inference and Optimal Design for the Sparse Linear Model
- Matthias W. Seeger; (26):759−813, 2008.
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- Finite-Time Bounds for Fitted Value Iteration
- Rémi Munos, Csaba Szepesvári; (27):815−857, 2008.
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- An Error Bound Based on a Worst Likely Assignment
- Eric Bax, Augusto Callejas; (28):859−891, 2008.
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- Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
- Mathias Drton, Thomas S. Richardson; (29):893−914, 2008.
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- Bouligand Derivatives and Robustness of Support Vector Machines for Regression
- Andreas Christmann, Arnout Van Messem; (30):915−936, 2008.
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- Accelerated Neural Evolution through Cooperatively Coevolved Synapses
- Faustino Gomez, Jürgen Schmidhuber, Risto Miikkulainen; (31):937−965, 2008.
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- Search for Additive Nonlinear Time Series Causal Models
- Tianjiao Chu, Clark Glymour; (32):967−991, 2008.
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- Shark
- Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; (33):993−996, 2008. (Machine Learning Open Source Software Paper)
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[code]
- Hit Miss Networks with Applications to Instance Selection
- Elena Marchiori; (34):997−1017, 2008.
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- Consistency of Trace Norm Minimization
- Francis R. Bach; (35):1019−1048, 2008.
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- Learning Similarity with Operator-valued Large-margin Classifiers
- Andreas Maurer; (36):1049−1082, 2008.
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- Ranking Categorical Features Using Generalization Properties
- Sivan Sabato, Shai Shalev-Shwartz; (37):1083−1114, 2008.
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- A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
- Zach Jorgensen, Yan Zhou, Meador Inge; (38):1115−1146, 2008.
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- Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
- Matthias W. Seeger; (39):1147−1178, 2008.
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- Consistency of the Group Lasso and Multiple Kernel Learning
- Francis R. Bach; (40):1179−1225, 2008.
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- Maximal Causes for Non-linear Component Extraction
- Jörg Lücke, Maneesh Sahani; (41):1227−1267, 2008.
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- Optimal Solutions for Sparse Principal Component Analysis
- Alexandre d'Aspremont, Francis Bach, Laurent El Ghaoui; (42):1269−1294, 2008.
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- Using Markov Blankets for Causal Structure Learning
- Jean-Philippe Pellet, André Elisseeff; (43):1295−1342, 2008.
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- A Bahadur Representation of the Linear Support Vector Machine
- Ja-Yong Koo, Yoonkyung Lee, Yuwon Kim, Changyi Park; (44):1343−1368, 2008.
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- Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
- Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin; (45):1369−1398, 2008.
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- Online Learning of Complex Prediction Problems Using Simultaneous Projections
- Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer; (46):1399−1435, 2008.
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- Causal Reasoning with Ancestral Graphs
- Jiji Zhang; (47):1437−1474, 2008.
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- Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming
- Ashwin Srinivasan, Ross D. King; (48):1475−1533, 2008.
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- Learning to Combine Motor Primitives Via Greedy Additive Regression
- Manu Chhabra, Robert A. Jacobs; (49):1535−1558, 2008.
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- Aggregation of SVM Classifiers Using Sobolev Spaces
- Sébastien Loustau; (50):1559−1582, 2008.
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- Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
- Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen; (51):1583−1614, 2008.
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- Universal Multi-Task Kernels
- Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying; (52):1615−1646, 2008.
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- A New Algorithm for Estimating the Effective Dimension-Reduction Subspace
- Arnak S. Dalalyan, Anatoly Juditsky, Vladimir Spokoiny; (53):1647−1678, 2008.
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- Value Function Based Reinforcement Learning in Changing Markovian Environments
- Balázs Csanád Csáji, László Monostori; (54):1679−1709, 2008.
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- Regularization on Graphs with Function-adapted Diffusion Processes
- Arthur D. Szlam, Mauro Maggioni, Ronald R. Coifman; (55):1711−1739, 2008.
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- Nearly Uniform Validation Improves Compression-Based Error Bounds
- Eric Bax; (56):1741−1755, 2008.
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- Learning from Multiple Sources
- Koby Crammer, Michael Kearns, Jennifer Wortman; (57):1757−1774, 2008.
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- Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
- Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett; (58):1775−1822, 2008.
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- Classification with a Reject Option using a Hinge Loss
- Peter L. Bartlett, Marten H. Wegkamp; (59):1823−1840, 2008.
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- Learning Balls of Strings from Edit Corrections
- Leonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini; (60):1841−1870, 2008.
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- LIBLINEAR: A Library for Large Linear Classification
- Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; (61):1871−1874, 2008. (Machine Learning Open Source Software Paper)
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[code]
- On Relevant Dimensions in Kernel Feature Spaces
- Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller; (62):1875−1908, 2008.
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- Manifold Learning: The Price of Normalization
- Yair Goldberg, Alon Zakai, Dan Kushnir, Ya'acov Ritov; (63):1909−1939, 2008.
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- Complete Identification Methods for the Causal Hierarchy
- Ilya Shpitser, Judea Pearl; (64):1941−1979, 2008.
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- Mixed Membership Stochastic Blockmodels
- Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing; (65):1981−2014, 2008.
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- Consistency of Random Forests and Other Averaging Classifiers
- Gérard Biau, Luc Devroye, Gábor Lugosi; (66):2015−2033, 2008.
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- Approximations for Binary Gaussian Process Classification
- Hannes Nickisch, Carl Edward Rasmussen; (67):2035−2078, 2008.
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- Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management
- Abraham George, Warren B. Powell, Sanjeev R. Kulkarni; (68):2079−2111, 2008.
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- Gradient Tree Boosting for Training Conditional Random Fields
- Thomas G. Dietterich, Guohua Hao, Adam Ashenfelter; (69):2113−2139, 2008.
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- HPB: A Model for Handling BN Nodes with High Cardinality Parents
- Jorge Jambeiro Filho, Jacques Wainer; (70):2141−2170, 2008.
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- A Moment Bound for Multi-hinge Classifiers
- Bernadetta Tarigan, Sara A. van de Geer; (71):2171−2185, 2008.
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- Ranking Individuals by Group Comparisons
- Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng; (72):2187−2216, 2008.
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- Forecasting Web Page Views: Methods and Observations
- Jia Li, Andrew W. Moore; (73):2217−2250, 2008.
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- Finding Optimal Bayesian Network Given a Super-Structure
- Eric Perrier, Seiya Imoto, Satoru Miyano; (74):2251−2286, 2008.
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- Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
- Manfred K. Warmuth, Dima Kuzmin; (75):2287−2320, 2008.
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- Probabilistic Characterization of Random Decision Trees
- Amit Dhurandhar, Alin Dobra; (76):2321−2348, 2008.
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- Learning to Select Features using their Properties
- Eyal Krupka, Amir Navot, Naftali Tishby; (77):2349−2376, 2008.
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- Model Selection in Kernel Based Regression using the Influence Function
- Michiel Debruyne, Mia Hubert, Johan A.K. Suykens; (78):2377−2400, 2008.
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- Non-Parametric Modeling of Partially Ranked Data
- Guy Lebanon, Yi Mao; (79):2401−2429, 2008.
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- On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix
- Xing Sun, Andrew B. Nobel; (80):2431−2453, 2008.
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- Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis
- Kun Zhang, Laiwan Chan; (81):2455−2487, 2008.
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- On the Equivalence of Linear Dimensionality-Reducing Transformations
- Marco Loog; (82):2489−2490, 2008.
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- SimpleMKL
- Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet; (83):2491−2521, 2008.
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- Active Learning of Causal Networks with Intervention Experiments and Optimal Designs
- Yang-Bo He, Zhi Geng; (84):2523−2547, 2008.
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- Stationary Features and Cat Detection
- François Fleuret, Donald Geman; (85):2549−2578, 2008.
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- Visualizing Data using t-SNE
- Laurens van der Maaten, Geoffrey Hinton; (86):2579−2605, 2008.
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- Model Selection for Regression with Continuous Kernel Functions Using the Modulus of Continuity
- Imhoi Koo, Rhee Man Kil; (87):2607−2633, 2008.
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- Multi-Agent Reinforcement Learning in Common Interest and Fixed Sum Stochastic Games: An Experimental Study
- Avraham Bab, Ronen I. Brafman; (88):2635−2675, 2008.
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- An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons
- Salvador García, Francisco Herrera; (89):2677−2694, 2008.
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- JNCC2: The Java Implementation Of Naive Credal Classifier 2
- Giorgio Corani, Marco Zaffalon; (90):2695−2698, 2008. (Machine Learning Open Source Software Paper)
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[code]
- Learning Bounded Treewidth Bayesian Networks
- Gal Elidan, Stephen Gould; (91):2699−2731, 2008.
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- Automatic PCA Dimension Selection for High Dimensional Data and Small Sample Sizes
- David C. Hoyle; (92):2733−2759, 2008.
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- Robust Submodular Observation Selection
- Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta; (93):2761−2801, 2008.
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- Magic Moments for Structured Output Prediction
- Elisa Ricci, Tijl De Bie, Nello Cristianini; (94):2803−2846, 2008.
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- Structural Learning of Chain Graphs via Decomposition
- Zongming Ma, Xianchao Xie, Zhi Geng; (95):2847−2880, 2008.
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