JMLR Volume 1

Learning with Mixtures of Trees
Marina Meila, Michael I. Jordan; 1(Oct):1-48, 2000.
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Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie; 1(Oct):49-75, 2000.
[abs] [pdf] [ps.gz] [ps] [html]

Learning Evaluation Functions to Improve Optimization by Local Search
Justin Boyan, Andrew W. Moore; 1(Nov):77-112, 2000.
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Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
Erin L. Allwein, Robert E. Schapire, Yoram Singer; 1(Dec):113-141, 2000.
[abs] [pdf] [ps.gz] [ps]

SVMTorch: Support Vector Machines for Large-Scale Regression Problems     (Kernel Machines Section)
Ronan Collobert, Samy Bengio; 1(Feb):143-160, 2001.
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Lagrangian Support Vector Machines     (Kernel Machines Section)
O. L. Mangasarian, David R. Musicant; 1(Mar):161-177, 2001.
[abs] [pdf] [ps.gz] [ps] [html]

Regularized Principal Manifolds     (Kernel Machines Section)
Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson; 1(Jun):179-209, 2001.
[abs] [pdf] [ps.gz] [ps]

Sparse Bayesian Learning and the Relevance Vector Machine
Michael E. Tipping; 1(Jun):211-244, 2001.
[abs] [pdf] [ps.gz] [ps]

Bayes Point Machines     (Kernel Machines Section)
Ralf Herbrich, Thore Graepel, Colin Campbell; 1(Aug):245-279, 2001.
[abs] [pdf] [ps.gz] [ps]

Tracking the Best Linear Predictor
Mark Herbster, Manfred K. Warmuth; 1(Sep):281-309, 2001.
[abs] [pdf] [ps.gz] [ps]

Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms
Robert E. Mahony, Robert C. Williamson; 1(Sep):311-355, 2001.
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