JMLR 2008 - PDF Preprint Papers

JMLR 2008

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JMLR
2008

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Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks

13 years 11 months ago

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Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...

Michael Collins, Amir Globerson, Terry Koo, Xavier...

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Id	Views	Title	Status

1	230	Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks	claim
2	209	Bayesian Inference and Optimal Design for the Sparse Linear Model	claim
3	198	Consistency of Random Forests and Other Averaging Classifiers	claim
4	188	Maximal Causes for Non-linear Component Extraction	claim
5	169	Multi-class Discriminant Kernel Learning via Convex Programming	claim
6	168	Max-margin Classification of Data with Absent Features	claim
7	159	Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies	claim
8	159	Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction	claim
9	151	Learning to Combine Motor Primitives Via Greedy Additive Regression	claim
10	150	Discriminative Learning of Max-Sum Classifiers	claim
11	148	Linear-Time Computation of Similarity Measures for Sequential Data	claim
12	144	Search for Additive Nonlinear Time Series Causal Models	claim
13	141	Accelerated Neural Evolution through Cooperatively Coevolved Synapses	claim
14	141	A New Algorithm for Estimating the Effective Dimension-Reduction Subspace	claim
15	141	Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models	claim
16	140	Aggregation of SVM Classifiers Using Sobolev Spaces	claim
17	139	Regularization on Graphs with Function-adapted Diffusion Processes	claim
18	137	Online Learning of Complex Prediction Problems Using Simultaneous Projections	claim
19	135	Complete Identification Methods for the Causal Hierarchy	claim
20	133	Algorithms for Sparse Linear Classifiers in the Massive Data Setting	claim
21	132	Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data	claim
22	131	On Relevant Dimensions in Kernel Feature Spaces	claim
23	129	Finite-Time Bounds for Fitted Value Iteration	claim
24	127	Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming	claim
25	124	Learning Control Knowledge for Forward Search Planning	claim
26	123	Optimization Techniques for Semi-Supervised Support Vector Machines	claim
27	120	Causal Reasoning with Ancestral Graphs	claim
28	117	Closed Sets for Labeled Data	claim
29	117	Active Learning by Spherical Subdivision	claim
30	116	Support Vector Machinery for Infinite Ensemble Learning	claim
31	114	Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines	claim
32	111	Ranking Categorical Features Using Generalization Properties	claim
33	110	Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods	claim
34	110	A Bahadur Representation of the Linear Support Vector Machine	claim
35	110	Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers	claim
36	108	A Recursive Method for Structural Learning of Directed Acyclic Graphs	claim
37	107	A Library for Locally Weighted Projection Regression	claim
38	104	Nearly Uniform Validation Improves Compression-Based Error Bounds	claim
39	104	Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2	claim
40	100	Hit Miss Networks with Applications to Instance Selection	claim
41	95	Learning Similarity with Operator-valued Large-margin Classifiers	claim
42	94	Using Markov Blankets for Causal Structure Learning	claim
43	92	Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective	claim
44	89	An Error Bound Based on a Worst Likely Assignment	claim
45	88	Universal Multi-Task Kernels	claim
46	84	LIBLINEAR: A Library for Large Linear Classification	claim
47	83	Evidence Contrary to the Statistical View of Boosting	claim
48	83	Generalization from Observed to Unobserved Features by Clustering	claim
49	83	Shark	claim
50	81	Bouligand Derivatives and Robustness of Support Vector Machines for Regression	claim
51	80	An Information Criterion for Variable Selection in Support Vector Machines	claim
52	79	Manifold Learning: The Price of Normalization	claim
53	76	A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters	claim
54	73	On the Suitable Domain for SVM Training in Image Coding	claim
55	69	Value Function Based Reinforcement Learning in Changing Markovian Environments	claim
56	69	Classification with a Reject Option using a Hinge Loss	claim

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