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UAI
2003

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Bayesian Hierarchical Mixtures of Experts

14 years 1 months ago

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The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classiﬁcation, based on soft probabilistic splits of the input space. In its o...

Christopher M. Bishop, Markus Svensén

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Id	Views	Title	Status

1	278	Bayesian Hierarchical Mixtures of Experts	claim
2	212	Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes	claim
3	176	A Simple Insight into Iterative Belief Propagation's Success	claim
4	174	Approximate Inference and Constrained Optimization	claim
5	172	On the Convergence of Bound Optimization Algorithms	claim
6	154	Learning Continuous Time Bayesian Networks	claim
7	154	Learning Generative Models of Similarity Matrices	claim
8	148	Marginalizing Out Future Passengers in Group Elevator Control	claim
9	144	Active Collaborative Filtering	claim
10	132	Decentralized Sensor Fusion with Distributed Particle Filters	claim
11	131	CLP(BN): Constraint Logic Programming for Probabilistic Knowledge	claim
12	128	A Linear Belief Function Approach to Portfolio Evaluation	claim
13	127	Updating with incomplete observations	claim
14	125	Robust Independence Testing for Constraint-Based Learning of Causal Structure	claim
15	122	Monte-Carlo optimizations for resource allocation problems in stochastic network systems	claim
16	122	On Local Optima in Learning Bayesian Networks	claim
17	121	Learning Riemannian Metrics	claim
18	118	Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation	claim
19	118	Layerwidth: Analysis of a New Metric for Directed Acyclic Graphs	claim
20	118	Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries	claim
21	116	Locally Weighted Naive Bayes	claim
22	116	Preference-based Graphic Models for Collaborative Filtering	claim
23	109	Policy-contingent abstraction for robust robot control	claim
24	109	Strong Faithfulness and Uniform Consistency in Causal Inference	claim
25	109	Markov Random Walk Representations with Continuous Distributions	claim
26	109	Exploiting Locality in Searching the Web	claim
27	108	On Information Regularization	claim
28	108	The Information Bottleneck EM Algorithm	claim
29	107	The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach	claim
30	106	Large-Sample Learning of Bayesian Networks is NP-Hard	claim
31	104	On revising fuzzy belief bases	claim
32	104	Optimal Limited Contingency Planning	claim
33	104	Renewal Strings for Cleaning Astronomical Databases	claim
34	102	Learning Module Networks	claim
35	100	Symbolic Generalization for On-line Planning	claim
36	100	Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models	claim
37	99	Systematic vs. Non-systematic Algorithms for Solving the MPE Task	claim
38	98	Web-Based Question Answering: A Decision-Making Perspective	claim
39	95	Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves	claim
40	91	Decision Making with Partially Consonant Belief Functions	claim
41	90	Sufficient Dimensionality Reduction with Irrelevance Statistics	claim
42	90	1 Billion Pages = 1 Million Dollars? Mining the Web to Play "Who Wants to be a Millionaire?"	claim
43	89	Loopy Belief Propagation as a Basis for Communication in Sensor Networks	claim
44	88	Learning Measurement Models for Unobserved Variables	claim
45	87	Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards	claim
46	86	An Importance Sampling Algorithm Based on Evidence Pre-propagation	claim
47	83	Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories	claim
48	83	A Logic for Reasoning about Evidence	claim
49	82	Practically Perfect	claim
50	78	On Triangulating Dynamic Graphical Models	claim
51	74	An Empirical Study of w-Cutset Sampling for Bayesian Networks	claim

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