Results 211 to 220 of about 26,331 (262)

Bayes-adaptive hierarchical MDPs

Applied Intelligence, 2016
Reinforcement learning (RL) is an area of machine learning that is concerned with how an agent learns to make decisions sequentially in order to optimize a particular performance measure. For achieving such a goal, the agent has to choose either 1) exploiting previously known knowledge that might end up at local optimality or 2) exploring to gather new
Vien, Ngo Anh, Lee, SeungGwan, Chung, TaeChoong   +2 more
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Hierarchical classification

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work showed that a simple hierarchy of Support Vectors Machines (SVM) with a top-down evaluation scheme has a surprisingly good performance on this kind of task.
Cesa Bianchi N, GENTILE, CLAUDIO, Zaniboni L.   +2 more
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C418. Fuzzy probability and hierarchical bayes

Journal of Statistical Computation and Simulation, 1994
I J Good
exaly   +2 more sources

Semi-hierarchical naïve Bayes classifier

2016 International Joint Conference on Neural Networks (IJCNN), 2016
The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data.
Hasna Njah, Salma Jamoussi, Walid Mahdi
openaire   +1 more source

Hierarchical Bayes approach for subgroup analysis

Statistical Methods in Medical Research, 2017
In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility.
Yu-Yi, Hsu, Jyoti, Zalkikar, Ram C, Tiwari   +2 more
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Bayes-Optimal Hierarchical Multilabel Classification

IEEE Transactions on Knowledge and Data Engineering, 2015
Hierarchical multilabel classification allows a sample to belong to multiple class labels residing on a hierarchy, which can be a tree or directed acyclic graph (DAG). However, popular hierarchical loss functions, such as the H-loss, can only be defined on tree hierarchies (but not on DAGs), and may also under- or over-penalize misclassifications near ...
Bi, Wei, Kwok, Jame Tin Yau
openaire   +2 more sources

Unscented Bayes Methods for Hierarchical Gaussian Processes

2020 Australian and New Zealand Control Conference (ANZCC), 2020
In this paper, we propose an unscented Bayes method for hierarchical Gaussian processes. The hierarchical Gaussian process consists of multiple layers of Gaussian process, which leads to intractable marginal likelihood and posterior distributions. Instead of resorting to the traditional sampling approach, we use the unscented transform to compute the ...
Mingliang Wang, Elling W. Jacobsen, Véronique Chotteau, Håkan Hjalmarsson   +3 more
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ON THE CONSISTENCY OF HIERARCHICAL BAYES ESTIMATORS

Statistics & Risk Modeling, 1996
Summary: In considering Bayesian estimation of multivariate normal mean, \textit{G. S. Datta} and \textit{M. Ghosh} [J. Stat. Plann. Inference 29, No. 3, 229-243 (1991; Zbl 0756.62014)] proposed hierarchical Bayes estimators and studied their asymptotic optimality property. A conjecture was raised therein.
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Decelerated Testing: A Hierarchical Bayes Approach

Technometrics, 2005
The problem discussed here has arisen from an industrial scenario involving the potential failure of an element of building structures. The element carries with it a warranty of several years. The scenario considered is not specific to buildings and occurs under other circumstances; it goes under the label “product stewardship.” Its statistical content,
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Hierarchical Multilabel Classification with Minimum Bayes Risk

2012 IEEE 12th International Conference on Data Mining, 2012
Hierarchical multilabel classification (HMC) allows an instance to have multiple labels residing in a hierarchy. A popular loss function used in HMC is the H-loss, which penalizes only the first classification mistake along each prediction path. However, the H-loss metric can only be used on tree-structured label hierarchies, but not on DAG hierarchies.
Wei Bi, James T. Kwok
openaire   +1 more source