Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Proceedings of the 1998 conference on Advances in neural information processing systems II
Maximum conditional likelihood via bound maximization and the CEM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
SMEM Algorithm for Mixture Models
Neural Computation
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Neural Processing Letters
Variational approximations in Bayesian model selection for finite mixture distributions
Computational Statistics & Data Analysis
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
A principled foundation for LCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
BM3E: Discriminative Density Propagation for Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Based on LVQ and a Split and Merge Procedure
Neural Information Processing
A Principled Foundation for LCS
Learning Classifier Systems
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
On similarities between inference in game theory and machine learning
Journal of Artificial Intelligence Research
Action oriented Bayesian learning of the operating space for a humanoid robot
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Fast Algorithm and Efficient Implementation of GMM-Based Pattern Classifiers
Journal of Signal Processing Systems
Simultaneous model selection and feature selection via BYY harmony learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
A new variational Bayesian algorithm with application to human mobility pattern modeling
Statistics and Computing
Bayesian hierarchical mixtures of experts
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Variational bayesian grammar induction for natural language
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Some aspects of latent structure analysis
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Structural Bayesian Linear Regression for Hidden Markov Models
Journal of Signal Processing Systems
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When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these problems based on the variational Bayesian (VB) framework. First, in the VB framework, we derive an objective function that can simultaneously optimize both model parameter distributions and model structure. Next, focusing on mixture models, we present a deterministic algorithm to approximately optimize the objective function by using the idea of the split and merge operations which we previously proposed within the maximum likelihood framework. Then, we apply the method to mixture of expers (MoE) models to experimentally show that the proposed method can find the optimal number of experts of a MoE while avoiding local maxima.