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ORCIDJournal of Machine Learning Research, Volume 1
Volume 1, 2000
- Marina Meila, Michael I. Jordan:
Learning with Mixtures of Trees. 1-48 - David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie:
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. 49-75 - Justin A. Boyan, Andrew W. Moore:
Learning Evaluation Functions to Improve Optimization by Local Search. 77-112 - Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. 113-141
Volume 1, 2001
- Ronan Collobert, Samy Bengio:
SVMTorch: Support Vector Machines for Large-Scale Regression Problems. 143-160 - Olvi L. Mangasarian, David R. Musicant:
Lagrangian Support Vector Machines. 161-177 - Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
Regularized Principal Manifolds. 179-209 - Michael E. Tipping:
Sparse Bayesian Learning and the Relevance Vector Machine. 211-244 - Ralf Herbrich, Thore Graepel, Colin Campbell:
Bayes Point Machines. 245-279 - Mark Herbster, Manfred K. Warmuth:
Tracking the Best Linear Predictor. 281-309 - Robert E. Mahony, Robert C. Williamson:
Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. 311-355
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