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ICML 2003 Workshop on Statistical Network Analysis

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ICML
2003
IEEE

270views Machine Learning» more ICML 2003»

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

14 years 5 months ago

Download www.aladdin.cs.cmu.edu

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...

Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty

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Id	Views	Title	Status

1	270	Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions	claim
2	249	Learning Distance Functions using Equivalence Relations	claim
3	201	Online Convex Programming and Generalized Infinitesimal Gradient Ascent	claim
4	199	Hidden Markov Support Vector Machines	claim
5	192	Regression Error Characteristic Curves	claim
6	192	Kernel PLS-SVC for Linear and Nonlinear Classification	claim
7	187	Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution	claim
8	183	Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic	claim
9	182	Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization	claim
10	181	Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach	claim
11	181	Decision-tree Induction from Time-series Data Based on a Standard-example Split Test	claim
12	180	Evolving Strategies for Focused Web Crawling	claim
13	178	Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers	claim
14	174	Error Bounds for Approximate Policy Iteration	claim
15	173	The Pre-Image Problem in Kernel Methods	claim
16	171	Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining	claim
17	170	Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning	claim
18	168	Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning	claim
19	167	Evolutionary MCMC Sampling and Optimization in Discrete Spaces	claim
20	166	Choosing Between Two Learning Algorithms Based on Calibrated Tests	claim
21	165	The Cross Entropy Method for Fast Policy Search	claim
22	161	Identifying Predictive Structures in Relational Data Using Multiple Instance Learning	claim
23	159	Tractable Bayesian Learning of Tree Augmented Naive Bayes Models	claim
24	158	Boosting Lazy Decision Trees	claim
25	158	Transductive Learning via Spectral Graph Partitioning	claim
26	157	Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors	claim
27	157	Action Elimination and Stopping Conditions for Reinforcement Learning	claim
28	157	An Analysis of Rule Evaluation Metrics	claim
29	157	Marginalized Kernels Between Labeled Graphs	claim
30	156	Incorporating Diversity in Active Learning with Support Vector Machines	claim
31	156	AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents	claim
32	151	Hierarchical Policy Gradient Algorithms	claim
33	150	Text Classification Using Stochastic Keyword Generation	claim
34	150	The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy	claim
35	148	Bayesian Network Anomaly Pattern Detection for Disease Outbreaks	claim
36	146	Semi-Supervised Learning of Mixture Models	claim
37	146	Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression	claim
38	146	TD(0) Converges Provably Faster than the Residual Gradient Algorithm	claim
39	146	Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation	claim
40	143	On Kernel Methods for Relational Learning	claim
41	142	Online Choice of Active Learning Algorithms	claim
42	142	The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics	claim
43	142	A Kernel Between Sets of Vectors	claim
44	142	Adaptive Overrelaxed Bound Optimization Methods	claim
45	140	Relational Instance Based Regression for Relational Reinforcement Learning	claim
46	140	Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries	claim
47	140	Using Linear-threshold Algorithms to Combine Multi-class Sub-experts	claim
48	140	Optimization with EM and Expectation-Conjugate-Gradient	claim
49	139	Flexible Mixture Model for Collaborative Filtering	claim
50	138	Design for an Optimal Probe	claim
51	137	Multi-Objective Programming in SVMs	claim
52	137	The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods	claim
53	137	BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games	claim
54	137	Online Ranking/Collaborative Filtering Using the Perceptron Algorithm	claim
55	137	Learning Mixture Models with the Latent Maximum Entropy Principle	claim
56	136	Characteristics of Long-term Learning in Soar and its Application to the Utility Problem	claim
57	135	Learning with Idealized Kernels	claim
58	134	The Set Covering Machine with Data-Dependent Half-Spaces	claim
59	134	Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning	claim
60	134	Eliminating Class Noise in Large Datasets	claim
61	133	Diffusion Approximation for Bayesian Markov Chains	claim
62	133	Hierarchical Latent Knowledge Analysis for Co-occurrence Data	claim
63	132	Low Bias Bagged Support Vector Machines	claim
64	131	A Faster Iterative Scaling Algorithm for Conditional Exponential Model	claim
65	130	Classification of Text Documents Based on Minimum System Entropy	claim
66	130	Weighted Order Statistic Classifiers with Large Rank-Order Margin	claim
67	129	Relativized Options: Choosing the Right Transformation	claim
68	129	Weighted Low-Rank Approximations	claim
69	129	Learning on the Test Data: Leveraging Unseen Features	claim
70	126	Informative Discriminant Analysis	claim
71	126	Linear Programming Boosting for Uneven Datasets	claim
72	124	Exploration in Metric State Spaces	claim
73	123	Correlated Q-Learning	claim
74	123	An Evaluation on Feature Selection for Text Clustering	claim
75	122	Online Feature Selection using Grafting	claim
76	121	Margin Distribution and Learning	claim
77	121	Planning in the Presence of Cost Functions Controlled by an Adversary	claim
78	121	Q-Decomposition for Reinforcement Learning Agents	claim
79	120	Stochastic Local Search in k-Term DNF Learning	claim
80	120	On the Convergence of Boosting Procedures	claim
81	119	Representational Issues in Meta-Learning	claim
82	119	Tackling the Poor Assumptions of Naive Bayes Text Classifiers	claim
83	119	Testing Exchangeability On-Line	claim
84	118	Isometric Embedding and Continuum ISOMAP	claim
85	117	Probabilistic Classifiers and the Concepts They Recognize	claim
86	117	Model-based Policy Gradient Reinforcement Learning	claim
87	114	Unsupervised Learning with Permuted Data	claim
88	114	A Loss Function Analysis for Classification Methods in Text Categorization	claim
89	113	Robust Induction of Process Models from Time-Series Data	claim
90	113	Learning with Knowledge from Multiple Experts	claim
91	111	Decision Tree with Better Ranking	claim
92	110	Cross-Entropy Directed Embedding of Network Data	claim
93	109	Perceptron Based Learning with Example Dependent and Noisy Costs	claim
94	109	SimpleSVM	claim
95	108	Avoiding Bias when Aggregating Relational Data with Degree Disparity	claim
96	108	Reinforcement Learning as Classification: Leveraging Modern Classifiers	claim
97	107	Goal-directed Learning to Fly	claim
98	106	Utilizing Domain Knowledge in Neuroevolution	claim
99	106	Link-based Classification	claim
100	105	Principled Methods for Advising Reinforcement Learning Agents	claim
101	104	Visual Learning by Evolutionary Feature Synthesis	claim
102	104	The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping	claim
103	103	Learning Logic Programs for Layout Analysis Correction	claim
104	98	Combining TD-learning with Cascade-correlation Networks	claim
105	97	Using the Triangle Inequality to Accelerate k-Means	claim
106	96	Mixtures of Conditional Maximum Entropy Models	claim
107	87	Text Bundling: Statistics Based Data-Reduction	claim

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