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ORCIDJournal of Machine Learning Research, Volume 9
Volume 9, 2008
- Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
Max-margin Classification of Data with Absent Features. 1-21 - Konrad Rieck, Pavel Laskov:
Linear-Time Computation of Similarity Measures for Sequential Data. 23-48 - Gustavo Camps-Valls, Jaime Gutierrez, Gabriel Gómez-Pérez, Jesus Malo:
On the Suitable Domain for SVM Training in Image Coding. 49-66 - Vojtech Franc, Bogdan Savchynskyy:
Discriminative Learning of Max-Sum Classifiers. 67-104 - Falk-Florian Henrich, Klaus Obermayer:
Active Learning by Spherical Subdivision. 105-130 - David Mease, Abraham J. Wyner:
Evidence Contrary to the Statistical View of Boosting. 131-156 - Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
Optimization Techniques for Semi-Supervised Support Vector Machines. 203-233 - Andreas Krause, Ajit Paul Singh, Carlos Guestrin:
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies. 235-284 - Hsuan-Tien Lin, Ling Li:
Support Vector Machinery for Infinite Ensemble Learning. 285-312 - Suhrid Balakrishnan, David Madigan:
Algorithms for Sparse Linear Classifiers in the Massive Data Setting. 313-337 - Eyal Krupka, Naftali Tishby:
Generalization from Observed to Unobserved Features by Clustering. 339-370 - Glenn Shafer, Vladimir Vovk:
A Tutorial on Conformal Prediction. 371-421 - Liviu Panait, Karl Tuyls
, Sean Luke:
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective. 423-457 - Xianchao Xie, Zhi Geng:
A Recursive Method for Structural Learning of Directed Acyclic Graphs. 459-483 - Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont:
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. 485-516 - Jieping Ye:
Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. 517-519 - Bo Jiang, Xuegong Zhang, Tianxi Cai:
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers. 521-540 - Gerda Claeskens, Christophe Croux, Johan Van Kerckhoven:
An Information Criterion for Variable Selection in Support Vector Machines. 541-558 - Gemma C. Garriga, Petra Kralj, Nada Lavrac:
Closed Sets for Labeled Data. 559-580 - Giorgio Corani, Marco Zaffalon:
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2. 581-621 - Stefan Klanke, Sethu Vijayakumar, Stefan Schaal:
A Library for Locally Weighted Projection Regression. 623-626 - Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi:
Trust Region Newton Method for Logistic Regression. 627-650 - Shann-Ching Chen, Geoffrey J. Gordon, Robert F. Murphy:
Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns. 651-682 - Sung Wook Yoon, Alan Fern, Robert Givan:
Learning Control Knowledge for Forward Search Planning. 683-718 - Jieping Ye, Shuiwang Ji, Jianhui Chen:
Multi-class Discriminant Kernel Learning via Convex Programming. 719-758 - Matthias W. Seeger:
Bayesian Inference and Optimal Design for the Sparse Linear Model. 759-813 - Rémi Munos, Csaba Szepesvári:
Finite-Time Bounds for Fitted Value Iteration. 815-857 - Eric Bax, Augusto Callejas:
An Error Bound Based on a Worst Likely Assignment. 859-891 - Mathias Drton, Thomas S. Richardson:
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. 893-914 - Andreas Christmann, Arnout Van Messem:
Bouligand Derivatives and Robustness of Support Vector Machines for Regression. 915-936 - Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen:
Accelerated Neural Evolution through Cooperatively Coevolved Synapses. 937-965 - Tianjiao Chu, Clark Glymour:
Search for Additive Nonlinear Time Series Causal Models. 967-991 - Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers:
Shark. 993-996 - Elena Marchiori:
Hit Miss Networks with Applications to Instance Selection. 997-1017 - Francis R. Bach:
Consistency of Trace Norm Minimization. 1019-1048 - Andreas Maurer:
Learning Similarity with Operator-valued Large-margin Classifiers. 1049-1082 - Sivan Sabato, Shai Shalev-Shwartz:
Ranking Categorical Features Using Generalization Properties. 1083-1114 - Zach Jorgensen, Yan Zhou, W. Meador Inge:
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters. 1115-1146 - Matthias W. Seeger:
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. 1147-1178 - Francis R. Bach:
Consistency of the Group Lasso and Multiple Kernel Learning. 1179-1225 - Jörg Lücke, Maneesh Sahani:
Maximal Causes for Non-linear Component Extraction. 1227-1267 - Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui:
Optimal Solutions for Sparse Principal Component Analysis. 1269-1294 - Jean-Philippe Pellet, André Elisseeff:
Using Markov Blankets for Causal Structure Learning. 1295-1342 - Ja-Yong Koo, Yoonkyung Lee, Yuwon Kim, Changyi Park:
A Bahadur Representation of the Linear Support Vector Machine. 1343-1368 - Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. 1369-1398 - Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer:
Online Learning of Complex Prediction Problems Using Simultaneous Projections. 1399-1435 - Jiji Zhang:
Causal Reasoning with Ancestral Graphs. 1437-1474 - Ashwin Srinivasan, Ross D. King:
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. 1475-1533 - Manu Chhabra, Robert A. Jacobs:
Learning to Combine Motor Primitives Via Greedy Additive Regression. 1535-1558 - Sébastien Loustau:
Aggregation of SVM Classifiers Using Sobolev Spaces. 1559-1582 - Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen:
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction. 1583-1614 - Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
Universal Multi-Task Kernels. 1615-1646 - Arnak S. Dalalyan, Anatoli B. Juditsky, Vladimir G. Spokoiny:
A New Algorithm for Estimating the Effective Dimension-Reduction Subspace. 1647-1678 - Balázs Csanád Csáji, László Monostori:
Value Function Based Reinforcement Learning in Changing Markovian Environments. 1679-1709 - Arthur D. Szlam, Mauro Maggioni, Ronald R. Coifman:
Regularization on Graphs with Function-adapted Diffusion Processes. 1711-1739 - Eric Bax:
Nearly Uniform Validation Improves Compression-Based Error Bounds. 1741-1755 - Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Multiple Sources. 1757-1774 - Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett:
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. 1775-1822 - Peter L. Bartlett, Marten H. Wegkamp:
Classification with a Reject Option using a Hinge Loss. 1823-1840 - Leonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini:
Learning Balls of Strings from Edit Corrections. 1841-1870 - Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin:
LIBLINEAR: A Library for Large Linear Classification. 1871-1874 - Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller:
On Relevant Dimensions in Kernel Feature Spaces. 1875-1908 - Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Ritov:
Manifold Learning: The Price of Normalization. 1909-1939 - Ilya Shpitser, Judea Pearl:
Complete Identification Methods for the Causal Hierarchy. 1941-1979 - Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
Mixed Membership Stochastic Blockmodels. 1981-2014 - Gérard Biau, Luc Devroye, Gábor Lugosi:
Consistency of Random Forests and Other Averaging Classifiers. 2015-2033
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