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ML 2002 Workshop on Machine Learrning

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ML
2002
ACM

246views Machine Learning» more ML 2002»

Bayesian Clustering by Dynamics

13 years 11 months ago

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This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to disc...

Marco Ramoni, Paola Sebastiani, Paul R. Cohen

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Id	Views	Title	Status

1	246	Bayesian Clustering by Dynamics	claim
2	223	Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?	claim
3	220	Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities	claim
4	180	Gene Selection for Cancer Classification using Support Vector Machines	claim
5	178	Metric-Based Methods for Adaptive Model Selection and Regularization	claim
6	168	On Average Versus Discounted Reward Temporal-Difference Learning	claim
7	167	Linear Programming Boosting via Column Generation	claim
8	163	Structural Modelling with Sparse Kernels	claim
9	154	Technical Update: Least-Squares Temporal Difference Learning	claim
10	146	Variable Resolution Discretization in Optimal Control	claim
11	146	Kernel Matching Pursuit	claim
12	145	Boosting Methods for Regression	claim
13	143	A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes	claim
14	142	Convergence of a Generalized SMO Algorithm for SVM Classifier Design	claim
15	141	On the Existence of Linear Weak Learners and Applications to Boosting	claim
16	140	A Probabilistic Framework for SVM Regression and Error Bar Estimation	claim
17	135	Bayesian Treed Models	claim
18	133	Finite-time Analysis of the Multiarmed Bandit Problem	claim
19	133	Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates	claim
20	129	Model Selection for Small Sample Regression	claim
21	128	A Simple Method for Generating Additive Clustering Models with Limited Complexity	claim
22	127	Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces	claim
23	123	Feature Generation Using General Constructor Functions	claim
24	121	Choosing Multiple Parameters for Support Vector Machines	claim
25	121	Near-Optimal Reinforcement Learning in Polynomial Time	claim
26	114	Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts	claim
27	111	Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data	claim
28	107	Training Invariant Support Vector Machines	claim
29	106	Statistical Properties and Adaptive Tuning of Support Vector Machines	claim
30	104	A Simple Decomposition Method for Support Vector Machines	claim
31	100	Structure in the Space of Value Functions	claim

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