Bayesian Methods for Artificial Intelligence and Machine Learning
Zoubin Ghahramani
- 27 Jun 2008
- Vol. 178, pp 8-8
6
TL;DR: This talk will introduce fundamental topics in Bayesian statistics as they apply to machine learning and AI, and address some misconceptions about Bayesian approaches.
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Abstract: Bayesian methods provide a framework for representing and manipulating uncertainty, for learning from noisy data, and for making decisions that maximize expected utility----components which are important to both AI and Machine Learning. However, although Bayesian methods have become more popular in recent years, there remains a good degree of skepticism with respect to taking a fully Bayesian approach. This talk will introduce fundamental topics in Bayesian statistics as they apply to machine learning and AI, and address some misconceptions about Bayesian approaches. I will then discuss some current work on non-parametric Bayesian machine learning, particularly in the area of unsupervised learning.
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References
A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm
TL;DR: A hybrid method is formed by using Nearest neighbor, Bayesian methods and genetic algorithms together to achieve successful results on classifying by eliminating data that make difficult to learn.
82
Bayesian Learning of Neural Networks for Mobile User Position Prediction
Sherif Akoush,Ahmed Sameh +1 more
- 24 Sep 2007
TL;DR: A novel technique for location prediction of mobile users has been proposed, and a paging technique based on it is developed, and results of the proposed Bayesian Neural Network are compared with 5 standard neural network techniques in predicting next location.
27
Nonparametric bayesian inference in nuclear spectrometry
Eric Barat,Thomas Dautremer,T. Montagu +2 more
- 01 Oct 2007
TL;DR: In this paper, a hierarchical polya tree-Dirichlet mixture of normal kernels is proposed for X/gamma-ray spectra estimation in the fields of nuclear physics.
17
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos
Yang Du,Chunfeng Yuan,Bing Li,Weiming Hu,Stephen J. Maybank +4 more
- 01 Jul 2017
TL;DR: Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.
•Dissertation
Machine learning techniques to estimate the dynamics of a slung load multirotor UAV system
Vargas Moreno,Aldo Enrique +1 more
- 01 Jan 2017
TL;DR: In this paper, the authors address the question of designing robust and flexible controllers to enable autonomous operation of a multirotor UAV with an attached slung load for general cargo transport.
9