检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

33 篇 会议
10 篇 期刊文献

馆藏范围

43 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

37 篇 工学
- 28 篇 计算机科学与技术...
- 26 篇 软件工程
- 10 篇 信息与通信工程
- 8 篇 生物医学工程（可授...
- 4 篇 安全科学与工程
- 3 篇 控制科学与工程
- 2 篇 机械工程
- 2 篇 光学工程
- 2 篇 仪器科学与技术
- 2 篇 生物工程
- 1 篇 冶金工程
- 1 篇 建筑学
19 篇 理学
- 16 篇 数学
- 10 篇 统计学（可授理学、...
- 3 篇 系统科学
- 2 篇 生物学
- 1 篇 物理学
13 篇 管理学
- 10 篇 图书情报与档案管...
- 2 篇 管理科学与工程(可...
- 2 篇 工商管理
- 1 篇 公共管理
5 篇 医学
- 5 篇 临床医学
- 4 篇 基础医学(可授医学...
- 4 篇 药学(可授医学、理...
- 2 篇 公共卫生与预防医...
2 篇 经济学
- 2 篇 应用经济学

主题

4 篇 hospitals
3 篇 predictive model...
3 篇 data models
2 篇 diagnosis
2 篇 bayes methods
2 篇 diseases
1 篇 learning systems
1 篇 support vector m...
1 篇 logistics
1 篇 video streaming
1 篇 cancer
1 篇 mood
1 篇 deep neural netw...
1 篇 indexes
1 篇 sparse matrices
1 篇 computer archite...
1 篇 big data
1 篇 anomaly detectio...
1 篇 vector spaces
1 篇 self-supervised ...

机构

12 篇 center for patte...
7 篇 center for patte...
6 篇 center for patte...
4 篇 institute for mu...
2 篇 department of el...
2 篇 monash universit...
2 篇 center for patte...
2 篇 shanghai key lab...
2 篇 center for patte...
2 篇 center for patte...
1 篇 department of ra...
1 篇 public health un...
1 篇 department of ph...
1 篇 department of in...
1 篇 department of co...
1 篇 department of co...
1 篇 university centr...
1 篇 national institu...
1 篇 institute of adv...
1 篇 institute of pub...

作者

22 篇 phung dinh
20 篇 venkatesh svetha
13 篇 nguyen tu dinh
11 篇 rana santu
7 篇 tran truyen
7 篇 svetha venkatesh
6 篇 le trung
6 篇 gupta sunil
6 篇 dinh phung
5 篇 nguyen vu
4 篇 he fan
4 篇 huang xiaolin
4 篇 vu hung
4 篇 he mingzhen
3 篇 li cheng
3 篇 gupta sunil kuma...
3 篇 luo wei
3 篇 truyen tran
2 篇 vo ba-ngu
2 篇 tu dinh nguyen

语言

43 篇 英文

检索条件"机构=Center for Pattern Recognition and Data Analytics"

共 43 条记录，以下是31-40 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Tensor-variate restricted boltzmann machines 29

Tensor-variate restricted boltzmann machines

引用

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

ISBN: (纸本)9781577357025

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 RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance. Copyright © 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Improved risk predictions via sparse imputation of patient c...

引用

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-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

关键词： Sparse matrices Hospitals Cancer Diseases Approximation methods Matrix decomposition Predictive models

来源：评论

学校读者我要写书评

暂无评论

Intervention-driven predictive framework for modeling healthcare data

Intervention-driven predictive framework for modeling health...

引用

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

ISBN: (纸本)9783319066073

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 may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

ISBN: (纸本)9781510811515

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 classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines. Copyright © SIAM.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

A random finite set model for data clustering 17

A random finite set model for data clustering

引用

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

ISBN: (纸本)9788490123553

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 not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data. © 2014 International Society of Information Fusion.

关键词： Mixtures

来源：评论

学校读者我要写书评

暂无评论

Regularizing Topic Discovery in EMRs with Side Information by Using Hierarchical Bayesian Models

Regularizing Topic Discovery in EMRs with Side Information b...

引用

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 relations, with an application on Electronic Medical Records (EMRs). Typically, a EMRs dataset consists of several patients (documents) and each patient contains many diagnosis codes (words). We exploit the side information available in the form of a semantic tree structure among the diagnosis codes for semantically-coherent disease topic discovery. We introduce novel functions to compute word-to-word distances when side information is available in the form of tree structures. We derive an efficient inference method for the wddCRF using MCMC technique. We evaluate on a real world medical dataset consisting of about 1000 patients with PolyVascular disease. Compared with the popular topic analysis tool, hierarchical Dirichlet process (HDP), our model discovers topics which are superior in terms of both qualitative and quantitative measures.

关键词： Diseases Bayes methods Medical diagnostic imaging Indexes Hospitals Correlation Predictive models

来源：评论

学校读者我要写书评

暂无评论

Split-merge augmented Gibbs sampling for Hierarchical Dirichlet Processes

引用

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

ISBN: (纸本)9783642374555

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 augment this method of inference, facilitating faster convergence. Whilst the incremental Gibbs sampler changes topic assignments of each word conditioned on the previous observations and model hyper-parameters, the split-merge sampler changes the topic assignments over a group of words in a single move. This allows efficient exploration of state space. We evaluate the proposed sampler on a synthetic test set and two benchmark document corpus and show that the proposed sampler enables the MCMC chain to converge faster to the desired stationary distribution. © Springer-Verlag 2013.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine 1

引用

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

ISBN: (数字)9783642374531

ISBN: (纸本)9783642374524

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 challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (***), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction. © Springer-Verlag 2013.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Learning parts-based representations with nonnegative restricted boltzmann machine 5

Learning parts-based representations with nonnegative restri...

引用

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, additive nature of the data. Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings. However, when it comes to parts-based discovery, RBMs do not usually produce satisfactory results. We enhance such capacity of RBMs by introducing nonnegativity into the model weights, resulting in a variant called nonnegative restricted Boltzmann machine (NRBM). The NRBM produces not only controllable decomposition of data into interpretable parts but also offers a way to estimate the intrinsic nonlinear dimensionality of data. We demonstrate the capacity of our model on well-known datasets of handwritten digits, faces and documents. The decomposition quality on images is comparable with or better than what produced by the nonnegative matrix factorisation (NMF), and the thematic features uncovered from text are qualitatively interpretable in a similar manner to that of the latent Dirichlet allocation (LDA). However, the learnt features, when used for classification, are more discriminative than those discovered by both NMF and LDA and comparable with those by RBM. © 2013 T.D. Nguyen, T. Tran, D. Phung & S. Venkatesh.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Learning sparse latent representation and distance metric for image retrieval

Learning sparse latent representation and distance metric fo...

引用

2013 IEEE International Conference on Multimedia and Expo, ICME 2013

ISBN: (纸本)9781479900152

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 textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG. © 2013 IEEE.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共5页 << < 1 2 3 4 5 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：