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检索条件"机构=Center for Pattern Recognition and Data Analytics"
43 条 记 录,以下是31-40 订阅
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Tensor-variate restricted boltzmann machines  29
Tensor-variate restricted boltzmann machines
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29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
作者: Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBM... 详细信息
来源: 评论
Improved risk predictions via sparse imputation of patient conditions in electronic medical records
Improved risk predictions via sparse imputation of patient c...
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International Conference on data Science and Advanced analytics (DSAA)
作者: Budhaditya Saha Sunil Gupta Svetha Venkatesh Center for Pattern Recognition and Data Analytics (PRaDA) Deakin University Australia
Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-m... 详细信息
来源: 评论
Intervention-driven predictive framework for modeling healthcare data
Intervention-driven predictive framework for modeling health...
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18th Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2014
作者: Rana, Santu Gupta, Sunil Kumar Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University 3216 Australia
Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this m... 详细信息
来源: 评论
Keeping up with innovation: A predictive framework for modeling healthcare data with evolving clinical interventions  14
Keeping up with innovation: A predictive framework for model...
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14th SIAM International Conference on data Mining, SDM 2014
作者: Gupta, Sunil Kumar Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Geelong Waura Ponds Campus Victoria Australia
Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single cla... 详细信息
来源: 评论
A random finite set model for data clustering  17
A random finite set model for data clustering
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17th International Conference on Information Fusion, FUSION 2014
作者: Phung, Dinh Vo, Ba-Ngu Center for Pattern Recognition and Data Analytics Deakin University Australia Department of Electrical and Computer Engineering Curtin University Australia
The goal of data clustering is to partition data points into groups to optimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is... 详细信息
来源: 评论
Regularizing Topic Discovery in EMRs with Side Information by Using Hierarchical Bayesian Models
Regularizing Topic Discovery in EMRs with Side Information b...
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International Conference on pattern recognition
作者: Cheng Li Santu Rana Dinh Phung Svetha Venkatesh Center for Pattern Recognition and Data Analytics Deakin University
We propose a novel hierarchical Bayesian framework, word-distance-dependent Chinese restaurant franchise (wd-dCRF) for topic discovery from a document corpus regularized by side information in the form of word-to-word... 详细信息
来源: 评论
Split-merge augmented Gibbs sampling for Hierarchical Dirichlet Processes
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17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013
作者: Rana, Santu Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia
The Hierarchical Dirichlet Process (HDP) model is an important tool for topic analysis. Inference can be performed through a Gibbs sampler using the auxiliary variable method. We propose a splitmerge procedure to augm... 详细信息
来源: 评论
Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine  1
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17th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2013
作者: Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong VIC Australia
Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The c... 详细信息
来源: 评论
Learning parts-based representations with nonnegative restricted boltzmann machine  5
Learning parts-based representations with nonnegative restri...
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5th Asian Conference on Machine Learning, ACML 2013
作者: Nguyeny, Tu Dinh Tranyz, Truyen Phungy, Dinh Venkateshy, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia
The success of any machine learning system depends critically on effective representations of data. In many cases, especially those in vision, it is desirable that a representation scheme uncovers the parts-based, add... 详细信息
来源: 评论
Learning sparse latent representation and distance metric for image retrieval
Learning sparse latent representation and distance metric fo...
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2013 IEEE International Conference on Multimedia and Expo, ICME 2013
作者: Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia
The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and ... 详细信息
来源: 评论