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

100 篇 会议
76 篇 期刊文献

馆藏范围

176 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

119 篇 工学
- 76 篇 计算机科学与技术...
- 71 篇 软件工程
- 42 篇 控制科学与工程
- 18 篇 生物工程
- 17 篇 信息与通信工程
- 17 篇 生物医学工程（可授...
- 13 篇 机械工程
- 12 篇 光学工程
- 7 篇 仪器科学与技术
- 6 篇 电气工程
- 6 篇 电子科学与技术（可...
- 6 篇 建筑学
- 6 篇 土木工程
- 5 篇 力学（可授工学、理...
- 4 篇 交通运输工程
- 4 篇 农业工程
- 3 篇 材料科学与工程（可...
71 篇 理学
- 36 篇 数学
- 26 篇 生物学
- 20 篇 统计学（可授理学、...
- 14 篇 物理学
- 9 篇 系统科学
27 篇 管理学
- 16 篇 管理科学与工程(可...
- 11 篇 图书情报与档案管...
- 4 篇 工商管理
7 篇 法学
- 7 篇 社会学
6 篇 医学
- 6 篇 临床医学
- 4 篇 基础医学(可授医学...
- 4 篇 药学(可授医学、理...
4 篇 农学
- 4 篇 作物学
2 篇 教育学
1 篇 军事学

主题

8 篇 reinforcement le...
7 篇 robots
7 篇 robot sensing sy...
5 篇 object detection
5 篇 hidden markov mo...
4 篇 cameras
4 篇 semantics
4 篇 machine learning
4 篇 laser radar
4 篇 kinematics
3 篇 safety
3 篇 three-dimensiona...
3 篇 markov processes
3 篇 brain
3 篇 learning algorit...
3 篇 computational mo...
3 篇 gradient methods
3 篇 navigation
3 篇 trajectory
3 篇 feature extracti...

机构

23 篇 machine learning...
23 篇 robotics institu...
13 篇 machine learning...
12 篇 robotics institu...
10 篇 machine learning...
8 篇 center for neuro...
8 篇 lamarr institute...
7 篇 robotics institu...
7 篇 machine learning...
6 篇 center for robot...
6 篇 neurotechnology ...
5 篇 center for techn...
4 篇 the department o...
4 篇 the lamarr insti...
4 篇 neuroscience and...
4 篇 neuroscience res...
4 篇 robotics institu...
4 篇 dahlem center fo...
3 篇 department of en...
3 篇 honda r&d co. lt...

作者

27 篇 schneider jeff
11 篇 jeff schneider
10 篇 stachniss cyrill
8 篇 neiswanger willi...
8 篇 mehta viraj
7 篇 char ian
5 篇 behley jens
5 篇 gordon geoffrey ...
5 篇 zhang yi
5 篇 huang tzu-kuo
5 篇 gordleeva susann...
4 篇 sami haddadin
4 篇 gupta saurabh
4 篇 savosenkov andre...
4 篇 chung youngseog
4 篇 guadagnino tizia...
4 篇 udoratina anna
4 篇 grigorev nikita
4 篇 marc toussaint
4 篇 xiong liang

语言

164 篇 英文
12 篇 其他

检索条件"机构=Department of Machine Learning and Robotics"

共 176 条记录，以下是161-170 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：

learning Multiple Tasks with a Sparse Matrix-Normal Penalty 10

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty

引用

Annual Conference on Neural Information Processing Systems

作者： Yi Zhang Jeff Schneider Machine Learning Department Carnegie Mellon University The Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781617823800

In this paper, we propose a matrix-variate normal penalty with sparse inverse co-variances to couple multiple tasks. learning multiple (parametric) models can be viewed as estimating a matrix of parameters, where rows and columns of the matrix correspond to tasks and features, respectively. Following the matrix-variate normal density, we design a penalty that decomposes the full covariance of matrix elements into the Kronecker product of row covariance and column covariance, which characterizes both task relatedness and feature representation. Several recently proposed methods are variants of the special cases of this formulation. To address the overfitting issue and select meaningful task and feature structures, we include sparse covariance selection into our matrix-normal regularization via ￡1 penalties on task and feature inverse covariances. We empirically study the proposed method and compare with related models in two real-world problems: detecting landmines in multiple fields and recognizing faces between different subjects. Experimental results show that the proposed framework provides an effective and flexible way to model various different structures of multiple tasks.

关键词： penalty Religious Missions orthogonality Covariance ANTITANK MINES column matrix learning

来源：评论

学校读者我要写书评

暂无评论

Projection penalties: Dimension reduction without loss

Projection penalties: Dimension reduction without loss

引用

27th International Conference on machine learning, ICML 2010

作者： Zhang, Yi Schneider, Jeff Machine Learning Department Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh PA 15213 United States

ISBN: (纸本)9781605589077

Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensionality. However, it can also be harmful because any reduction loses information. In this paper, we propose the projection penalty framework to make use of dimension reduction without losing valuable information. Reducing the feature space before learning predictive models can be viewed as restricting the model search to some parameter sub-space. The idea of projection penalties is that instead of restricting the search to a parameter subspace, we can search in the full space but penalize the projection distance to this subspace. Dimension reduction is used to guide the search, rather than to restrict it. We propose projection penalties for linear dimension reduction, and then generalize to kernel-based reduction and other nonlinear methods. We test projection penalties with various dimension reduction techniques in different prediction tasks, including principal component regression and partial least squares in regression tasks, kernel dimension reduction in face recognition, and latent topic modeling in text classification. Experimental results show that projection penalties are a more effective and reliable way to make use of dimension reduction techniques. Copyright 2010 by the author(s)/owner(s).

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

learning the semantic correlation: An alternative way to gain from unlabeled text

Learning the semantic correlation: An alternative way to gai...

引用

22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

作者： Zhang, Yi Schneider, Jeff Dubrawski, Artur Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781605609492

In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a generally transferable structure of the language and propose a new method to learn this structure using an appropriately chosen latent variable model. This semantic correlation contains structural information of the language space and can be used to control the joint shrinkage of model parameters for any specific task in the same space through regularization. In an empirical study, we construct 190 different text classification tasks from a real-world benchmark, and the unlabeled documents are a mixture from all these tasks. We test the ability of various algorithms to use the mixed unlabeled text to enhance all classification tasks. Empirical results show that the proposed approach is a reliable and scalable method for semi-supervised learning, regardless of the source of unlabeled data, the specific task to be enhanced, and the prediction model used.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

learning linear dynamical systems without sequence information 09

Learning linear dynamical systems without sequence informati...

引用

26th Annual International Conference on machine learning, ICML'09

作者： Huang, Tzu-Kuo Schneider, Jeff Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 United States

ISBN: (纸本)9781605585161

Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of data generated from the dynamic system. In this paper we consider the case where the data is not sequenced. The learning algorithm is presented a set of data points from the system's operation but with no temporal ordering. The data are simply drawn as individual disconnected points. While making this assumption may seem absurd at first glance, we observe that many scientic modeling tasks have exactly this property. In this paper we restrict our attention to learning linear, discrete time models. We propose several algorithms for learning these models based on optimizing approximate likelihood functions and test the methods on several synthetic data sets. Copyright 2009.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Zero-shot learning with semantic output codes

Zero-shot learning with semantic output codes

引用

23rd Annual Conference on Neural Information Processing Systems, NIPS 2009

作者： Palatucci, Mark Pomerleau, Dean Hinton, Geoffrey Mitchell, Tom M. Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States Intel Labs Pittsburgh PA 15213 United States Computer Science Department University of Toronto Toronto ON M5S 3G4 Canada Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9781615679119

We consider the problem of zero-shot learning, where the goal is to learn a classifier f: X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Zero-shot learning with semantic output codes 09

Zero-shot learning with semantic output codes

引用

Proceedings of the 23rd International Conference on Neural Information Processing Systems

作者： Mark Palatucci Dean Pomerleau Geoffrey Hinton Tom M. Mitchell Robotics Institute Carnegie Mellon University Pittsburgh PA Intel Labs Pittsburgh PA Computer Science Department University of Toronto Toronto Ontario Canada Machine Learning Department Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781615679119

We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning the semantic correlation: an alternative way to gain from unlabeled text 08

Learning the semantic correlation: an alternative way to gai...

引用

Proceedings of the 22nd International Conference on Neural Information Processing Systems

作者： Yi Zhang Jeff Schneider Artur Dubrawski Machine Learning Department Carnegie Mellon University The Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781605609492

关键词：

来源：评论

学校读者我要写书评

暂无评论

A constraint generation approach to learning stable linear dynamical systems

A constraint generation approach to learning stable linear d...

引用

21st Annual Conference on Neural Information Processing Systems, NIPS 2007

作者： Siddiqi, Sajid M. Boots, Byron Gordon, Geoffrey J. Robotics Institute Carnegie-Mellon University Pittsburgh PA 15213 United States Computer Science Department Carnegie-Mellon University Pittsburgh PA 15213 United States Machine Learning Department Carnegie-Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)160560352X

Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approximation of the problem as a convex program, start with a solution to a relaxed version of the program, and incrementally add constraints to improve stability. Rather than continuing to generate constraints until we reach a feasible solution, we test stability at each step;because the convex program is only an approximation of the desired problem, this early stopping rule can yield a higher-quality solution. We apply our algorithm to the task of learning dynamic textures from image sequences as well as to modeling biosurveillance drug-sales data. The constraint generation approach leads to noticeable improvement in the quality of simulated sequences. We compare our method to those of Lacy and Bernstein [1, 2], with positive results in terms of accuracy, quality of simulated sequences, and efficiency.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Fast planning for dynamic preferences

Fast planning for dynamic preferences

引用

18th International Conference on Automated Planning and Scheduling, ICAPS 2008

作者： Ziebart, Brian D. Dey, Anind K. Bagnell, J. Andrew Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9781577353867

We present an algorithm that quickly finds optimal plans for unforeseen agent preferences within graph-based planning domains where actions have deterministic outcomes and action costs are linearly parameterized by preference parameters. We focus on vehicle route planning for drivers with personal trade-offs for different types of roads, and specifically on settings where these preferences are not known until planning time. We employ novel bounds (based on the triangle inequality and on the the concavity of the optimal plan cost in the space of preferences) to enable the reuse of previously computed optimal plans that are similar to the new plan preferences. The resulting lower bounds are employed to guide the search for the optimal plan up to 60 times more efficiently than previous methods. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

A latent space approach to dynamic embedding of co-occurrence data

A latent space approach to dynamic embedding of co-occurrenc...

引用

11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Sarkar, Purnamrita Siddiqi, Sajid M. Gordon, Geoffrey J. Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States

We consider dynamic co-occurrence data, such as author-word links in papers published in successive years of the same conference. For static co-occurrence data, researchers often seek an embedding of the entities (authors and words) into a lowdimensional Euclidean space. We generalize a recent static co-occurrence model, the CODE model of Globerson et al. (2004), to the dynamic setting: we seek coordinates for each entity at each time step. The coordinates can change with time to explain new observations, but since large changes are improbable, we can exploit data at previous and subsequent steps to find a better explanation for current observations. To make inference tractable, we show how to approximate our observation model with a Gaussian distribution, allowing the use of a Kalman filter for tractable inference. The result is the first algorithm for dynamic embedding of co-occurrence data which provides distributional information for its coordinate estimates. We demonstrate our model both on synthetic data and on author-word data from the NIPS corpus, showing that it produces intuitively reasonable embeddings. We also provide evidence for the usefulness of our model by its performance on an authorprediction task.

关键词：

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18页 << < 9 10 11 12 13 14 15 16 17 18 > >>

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：