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

78 篇 会议
1 篇 期刊文献

馆藏范围

79 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

34 篇 工学
- 31 篇 电气工程
- 16 篇 计算机科学与技术...
- 10 篇 信息与通信工程
- 8 篇 软件工程
- 1 篇 光学工程
- 1 篇 电子科学与技术（可...
- 1 篇 控制科学与工程
- 1 篇 测绘科学与技术
- 1 篇 化学工程与技术
7 篇 医学
- 7 篇 临床医学
5 篇 理学
- 3 篇 物理学
- 1 篇 数学
- 1 篇 地理学
- 1 篇 系统科学
- 1 篇 统计学（可授理学、...
1 篇 管理学
- 1 篇 图书情报与档案管...

主题

16 篇 machine learning
11 篇 wireless communi...
11 篇 training
10 篇 signal processin...
7 篇 signal processin...
7 篇 accuracy
6 篇 signal to noise ...
5 篇 convergence
5 篇 optimization
5 篇 servers
5 篇 computational mo...
5 篇 time series anal...
5 篇 data models
4 篇 deep learning
4 篇 feature extracti...
4 篇 kernel
4 篇 estimation
4 篇 federated learni...
4 篇 yttrium
3 篇 noise measuremen...

机构

3 篇 ece department n...
2 篇 thales airborne ...
2 篇 univ padua dept ...
2 篇 department of el...
2 篇 institut univers...
2 篇 graduate program...
2 篇 avea labs istanb...
2 篇 eurecom sophia a...
1 篇 university of pi...
1 篇 institute for th...
1 篇 ucl gatsby compu...
1 篇 univ new south w...
1 篇 univ lancaster c...
1 篇 laboratory of ps...
1 篇 univ texas austi...
1 篇 signal processin...
1 篇 peking univ natl...
1 篇 univ carlos iii ...
1 篇 department of el...
1 篇 univ carlos iii ...

作者

3 篇 suleyman s. koza...
3 篇 ibrahim delibalt...
2 篇 deniz erdogmus
2 篇 sergey plis
2 篇 n. denizcan vanl...
2 篇 vince calhoun
2 篇 muhammed o. sayi...
1 篇 li xinyue
1 篇 negliat giovanni
1 篇 prato m.
1 篇 sylvain bouix
1 篇 ravishankar saip...
1 篇 kristoffer h. ma...
1 篇 joonyoung cho
1 篇 dimitrios katsel...
1 篇 robert jenssen
1 篇 d. erdogmus
1 篇 huseyin ozkan
1 篇 ruslan v. kutluy...
1 篇 schenato luca

语言

79 篇 英文

检索条件"任意字段=25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015"

共 79 条记录，以下是1-10 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：

ieee international workshop on machine learning for signal processing, mlsp

IEEE International Workshop on Machine Learning for Signal P...

引用

25th ieee international workshop on machine learning for signal processing, mlsp 2015

ISBN: (纸本)9781467374545

the proceedings contain 73 papers. the topics discussed include: discriminating bipolar disorder from major depression using whole-brain functional connectivity: a feature selection analysis with SVM-FOBA algorithm;characterization of diabetic peripheral neuropathy in infrared video sequences using independent component analysis;active learning for efficient querying from a human oracle with noisy response in a language model assisted brain computer interface;maximum a posteriori estimation of relative abundances of protein conformations;synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia;multiview classification of brain data through tensor factorization;an efficient multi-resolution SVM network approach for object detection in aerial images;and semi-supervised color decomposition for histopathological images using exclusive component analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning of scanning strategies for electronic support using predictive state representations 25

Learning of scanning strategies for electronic support using...

引用

25th ieee international workshop on machine learning for signal processing, mlsp 2015

作者： Claude, Hadrien Enderli, Cyrille Grandin, Jean-Francois Pietquin, Olivier Thales Airborne Systems Elancourt France Univ. Lille CRIStAL SequeL Team Villeneuve d'Ascq France Institut Universitaire de France France

ISBN: (纸本)9781467374545

In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow bandwidth. To maintain surveillance over the whole spectrum, the sensor has to sweep between frequency bands but requires a scanning strategy. Search strategies are usually designed prior to the mission using an approximate knowledge of illumination patterns. this often results in open-loop policies that cannot take advantage of previous observations. As pointed out in past researches, these strategies lack of robustness to the prior. We propose a new closed loop search strategy that learns a stochastic model of each radar using predictive state representations. the learning algorithm benefits from the recent advances in spectral learning and rank minimization using nuclear norm penalization. © 2015 ieee.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

GENERIC BOUNDS ON thE MAXIMUM DEVIATIONS IN SEQUENTIAL PREDICTION: AN INFORMATION-thEORETIC ANALYSIS 29

GENERIC BOUNDS ON THE MAXIMUM DEVIATIONS IN SEQUENTIAL PREDI...

引用

ieee 29th international workshop on machine learning for signal processing (mlsp)

作者： Fang, Song Zhu, Quanyan NYU Dept Elect & Comp Engn New York NY 10003 USA

ISBN: (纸本)9781728108247

In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. the fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

关键词： Information-theoretic learning sequential learning sequential prediction bounds on performance sequence prediction

来源：评论

学校读者我要写书评

暂无评论

INTRA-CLUSTER TRAINING STRATEGY FOR DEEP learning WIth APPLICATIONS TO LANGUAGE IDENTIFICATION 26

INTRA-CLUSTER TRAINING STRATEGY FOR DEEP LEARNING WITH APPLI...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Bekker, Alan Joseph Opher, Irit Lapidot, Itsik Goldberger, Jacob Bar Ilan Univ Fac Engn Ramat Gan Israel Afeka Acad Coll Engn ACLP Tel Aviv Israel

ISBN: (纸本)9781509007462

In this study we address the problem of training a neural network for language identification using speech samples in the form of i-vectors. Our approach involves training a classifier and analyzing the obtained confusion matrix. We cluster the languages by simultaneously clustering the columns and the rows of the confusion matrix. the language clusters are then used to define a modified cost function for training a neural-network that focuses on distinguishing between the true language and languages within the same cluster. the results show enhanced language identification on the NIST 2015 language identification dataset.

关键词： Confusion matrix clustering language identification

来源：评论

学校读者我要写书评

暂无评论

SCALABLE TRANSFORMED ADDITIVE signal DECOMPOSITION BY NON-CONJUGATE GAUSSIAN PROCESS INFERENCE 26

SCALABLE TRANSFORMED ADDITIVE SIGNAL DECOMPOSITION BY NON-CO...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Adam, Vincent Hensman, James Sahani, Maneesh UCL Gatsby Computat Neurosci Unit 25 Howland St London W1T 4JG England Univ Lancaster CHICAS Fac Hlth & Med Lancaster LA1 4YB England

ISBN: (纸本)9781509007462

Many functions and signals of interest are formed by the addition of multiple underlying components, often nonlinearly transformed and modified by noise. Examples may be found in the literature on Generalized Additive Models [1] and Underdetermined Source Separation [2] or other mode decomposition techniques. Recovery of the underlying component processes often depends on finding and exploiting statistical regularities within them. Gaussian Processes (GPs) [3] have become the dominant way to model statistical expectations over functions. Recent advances make inference of the GP posterior efficient for large scale datasets and arbitrary likeli-hoods [4, 5]. Here we extend these methods to the additive GP case [6, 7], thus achieving scalable marginal posterior inference over each latent function in settings such as those above.

关键词： Source separation

来源：评论

学校读者我要写书评

暂无评论

GLOBALLY OPTIMAL ROBUST MATRIX COMPLETION BASED ON M-ESTIMATION 30

GLOBALLY OPTIMAL ROBUST MATRIX COMPLETION BASED ON M-ESTIMAT...

引用

30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Ruppel, Felicia Muma, Michael Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp Merckstr 25 D-64283 Darmstadt Germany

ISBN: (纸本)9781728166629

Robust matrix completion allows for estimating a low-rank matrix based on a subset of its entries, even in presence of impulsive noise and outliers. We explore recent progress in the theoretical analysis of non-convex low-rank factorization problems to develop a robust approach that is based on a fast factorization method. We propose an algorithm that uses joint regression and scale estimation to compute the estimates. We prove that our algorithm converges to a global minimum with random initialization. An example function for which the guarantees hold is the pseudo-Huber function. In simulations, the proposed approach is compared to state-of the art robust and non-robust methods. In addition, its applicability to image inpainting and occlusion removal is demonstrated.

关键词： Matrix completion optimality low-rank factorization robustness image inpainting occlusion removal

来源：评论

学校读者我要写书评

暂无评论

COLLISION-FREE UAV NAVIGATION WIth A MONOCULAR CAMERA USING DEEP REINFORCEMENT learning 30

COLLISION-FREE UAV NAVIGATION WITH A MONOCULAR CAMERA USING ...

引用

30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Chen, Yun Gonzalez-Prelcic, Nuria Heath, Robert W., Jr. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781728166629

Small unmanned aerial vehicles (UAV) with reduced sensing and communication capabilities can support potential use cases in different indoor environments such as automated factories or commercial buildings. In this context, we consider the problem of collision-free autonomous UAV navigation supported by a simple sensor. We propose a navigation system based on object detection and deep reinforcement learning (DRL) that only exploits sensing data obtained by a monocular camera mounted on the UAV. Object detection is incorporated into DRL training to reduce flight time and to maximize the probability of avoiding both current and future crashes. Moreover, object detection also helps to remove the impact of wrong predictions from the deep network. When compared to schemes using traditional RL methods, the proposed framework not only leads to collision-free trips, but it also reduces flying times towards given destinations by 25%, and cuts down 50% of unnecessary turns.

关键词： Deep reinforcement learning UAV navigation Collision avoidance

来源：评论

学校读者我要写书评

暂无评论

DEEP REINFORCEMENT learning BASED ENERGY BEAMFORMING FOR POWERING SENSOR NETWORKS 29

DEEP REINFORCEMENT LEARNING BASED ENERGY BEAMFORMING FOR POW...

引用

ieee 29th international workshop on machine learning for signal processing (mlsp)

作者： Ozcelikkale, Ayca Koseoglu, Mehmet Srivastava, Mani Ahlen, Anders Uppsala Univ Signals & Syst Uppsala Sweden Hacettepe Univ Dept Comp Sci Ankara Turkey Univ Calif Dept Elect & Comp Engn Davis CA USA

ISBN: (纸本)9781728108247

We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. the aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized. We illustrate how RL can approach the optimum performance without explicitly forming a system model, but suffers from slow convergence. We also quantify the importance of the number of antennas at the energy beamformer and the number of sensors.

关键词： Wireless communications energy harvesting resource allocation reinforcement learning neural networks

来源：评论

学校读者我要写书评

暂无评论

LATENT FORCE MODELS FOR EARth OBSERVATION TIME SERIES PREDICTION 26

LATENT FORCE MODELS FOR EARTH OBSERVATION TIME SERIES PREDIC...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Luengo, David Campos-Taberner, Manuel Camps-Valls, Gustau Univ Politecn Madrid Dept Signal Theory & Commun Madrid Spain Univ Valencia Dept Earth Phys & Thermodynam E-46003 Valencia Spain Univ Valencia IPL E-46003 Valencia Spain

ISBN: (纸本)9781509007462

We introduce latent force models for Earth observation time series analysis. the model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. the LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.

关键词： Latent force models Gaussian processes Remote sensing satellite images Moderate Resolution Imaging Spectroradiometer (MODIS) gap filling multi-output regression rice crop monitoring

来源：评论

学校读者我要写书评

暂无评论

CORRENTROPY INDUCED METRIC BASED COMMON SPATIAL PATTERNS

CORRENTROPY INDUCED METRIC BASED COMMON SPATIAL PATTERNS

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Dong, Jiyao Chen, Badong Lu, Na Wang, Haixian Zheng, Nanning Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Southeast Univ Res Ctr Learning Sci Inst Child Dev & Educ Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509063413

Common spatial patterns ( CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. the goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. the conventional CSP is however sensitive to outliers because it is based on the L-2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm, called CIM based CSP (CSP-CIM), to improve the robustness of CSP with respect to outliers. the CSP-CIM searches the optimal solution by a simple gradient based iterative algorithm. A toy example and a real EEG dataset are used to demonstrate the desirable performance of the new method.

关键词： Common spatial patterns robustness correntropy induced metric kernel bandwidth

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8页 << < 1 2 3 4 5 6 7 8 > >>

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：