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Improved acoustic modeling with Bayesian learning
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992The authors study the use of Bayesian learning for the estimation of the parameters of a multivariate mixture Gaussian density. For speech recognition algorithms based on the continuous density hidden Markov model (CDHMM) framework, Bayesian learning serves as a unified approach for the following four applications: parameter smoothing, speaker ...
J.-L. Gauvain, C.-H. Lee
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Bayesian model learning based on a parallel MCMC strategy
Statistics and computing, 2006J. Corander, M. Gyllenberg, T. Koski
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Nonparametric Bayesian Models for Unsupervised Learning
2011Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as such, a challenging one: no ground-truth that can be used to validate clustering results is ...
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Scalable Graph-Based Semi-Supervised Learning through Sparse Bayesian Model
IEEE Transactions on Knowledge and Data Engineering, 2017Bingbing Jiang +3 more
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A Bayesian hierarchical model for learning natural scene categories
Computer Vision and Pattern Recognition, 2005Li Fei-Fei, P. Perona
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A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties
IEEE transactions on industrial electronics (1982. Print), 2021Gaoyang Li +4 more
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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Learning NAT-Modeled Bayesian Network Structures with Bayesian Approach
Proceedings of the Canadian Conference on Artificial Intelligence, 2022Yang Xiang, Wanrong Sun
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Nonparametric Bayesian Modelling in Machine Learning
2016Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space.
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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