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

57,254 篇 会议
184 册 图书

馆藏范围

57,431 篇 电子文献
7 种 纸本馆藏

日期分布

学科分类号

57,042 篇 工学
- 56,774 篇 计算机科学与技术...
- 7,556 篇 控制科学与工程
- 2,463 篇 软件工程
- 2,057 篇 电气工程
- 483 篇 信息与通信工程
- 360 篇 生物医学工程（可授...
- 67 篇 机械工程
- 57 篇 土木工程
- 23 篇 石油与天然气工程
- 18 篇 生物工程
- 15 篇 仪器科学与技术
4,454 篇 理学
- 3,698 篇 数学
- 368 篇 物理学
- 274 篇 生物学
- 53 篇 科学技术史(分学科...
- 52 篇 统计学（可授理学、...
- 22 篇 系统科学
- 18 篇 化学
2,190 篇 医学
- 1,233 篇 基础医学(可授医学...
- 1,065 篇 临床医学
- 36 篇 特种医学
2,181 篇 文学
- 2,178 篇 外国语言文学
1,355 篇 管理学
- 1,256 篇 管理科学与工程(可...
- 84 篇 图书情报与档案管...
1,231 篇 教育学
- 709 篇 教育学
- 507 篇 心理学(可授教育学...
- 15 篇 体育学
57 篇 法学
- 56 篇 社会学
36 篇 艺术学
- 36 篇 音乐与舞蹈学
35 篇 哲学
- 35 篇 哲学
13 篇 经济学
1 篇 农学
1 篇 军事学

主题

1,258 篇 artificial intel...
1,112 篇 machine learning
978 篇 deep learning
919 篇 data mining
780 篇 multi agent syst...
725 篇 semantics
527 篇 classification
510 篇 reinforcement le...
474 篇 learning systems
463 篇 clustering
439 篇 neural networks
415 篇 ontology
353 篇 natural language...
257 篇 computers
249 篇 rough sets
236 篇 fuzzy sets
230 篇 feature selectio...
219 篇 computer circuit...
213 篇 knowledge repres...
205 篇 logic programmin...

机构

191 篇 univ chinese aca...
107 篇 chinese acad sci...
65 篇 harbin inst tech...
63 篇 polish acad sci ...
62 篇 wroclaw univ sci...
59 篇 soochow univ sch...
55 篇 delft univ techn...
53 篇 carnegie mellon ...
48 篇 beijing univ tec...
44 篇 chinese acad sci...
44 篇 shanghai jiao to...
43 篇 tu wien austria
42 篇 univ sci & techn...
41 篇 czech tech univ ...
40 篇 univ ulsan sch e...
39 篇 beijing jiaotong...
38 篇 tsinghua univ de...
38 篇 north carolina s...
37 篇 nanjing univ sta...
36 篇 tsinghua univ sc...

作者

47 篇 gelbukh alexande...
42 篇 wang lei
36 篇 reis luis paulo
35 篇 pelachaud cather...
35 篇 malerba donato
34 篇 liu yang
33 篇 novais paulo
33 篇 cpalka krzysztof
32 篇 li li
30 篇 ricci alessandro
27 篇 lau nuno
26 篇 wang hui
26 篇 napoli amedeo
25 篇 boryczka urszula
25 篇 rutkowski leszek
25 篇 wang jing
25 篇 pluhacek michal
25 篇 bickmore timothy
25 篇 calvo hiram
24 篇 villasenor-pined...

语言

57,321 篇 英文
116 篇 中文
1 篇 丹麦文

检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是4971-4980 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：

Verified Correctness, Accuracy, and Convergence of a Stationary Iterative Linear Solver: Jacobi Method 16th

Verified Correctness, Accuracy, and Convergence of a Station...

引用

16th International Conference on Intelligent Computer Mathematics (CICM)

作者： Tekriwal, Mohit Appel, Andrew W. Kellison, Ariel E. Bindel, David Jeannin, Jean-Baptiste Univ Michigan Ann Arbor MI 48109 USA Princeton Univ Princeton NJ 08544 USA Cornell Univ Ithaca NY USA

ISBN: (纸本)9783031427527;9783031427534

Solving a sparse linear system of the form Ax = b is a common engineering task, e.g., as a step in approximating solutions of differential equations. Inverting a large matrix A is often too expensive, and instead engineers rely on iterative methods, which progressively approximate the solution x of the linear system in several iterations, where each iteration is a much less expensive (sparse) matrix-vector multiplication. We present a formal proof in the Coq proof assistant of the correctness, accuracy and convergence of one prominent iterative method, the Jacobi iteration. The accuracy and convergence properties of Jacobi iteration are well-studied, but most past analyses were performed in real arithmetic;instead, we study those properties, and prove our results, in floating-point arithmetic. We then show that our results are properly reflected in a concrete implementation in the C language. Finally, we show that the iteration will not overflow, under assumptions that we make explicit. Notably, our proofs are faithful to the details of the implementation, including C program semantics and floating-point arithmetic.

关键词： Formal Verification Numerical Methods Jacobi Method

来源：评论

学校读者我要写书评

暂无评论

Explainable AI for Medical Event Prediction for Heart Failure Patients 21st

Explainable AI for Medical Event Prediction for Heart Failur...

引用

21st International Conference on artificial intelligence in Medicine (AIME)

作者： Wrazen, Weronika Gontarska, Kordian Grzelka, Felix Polze, Andreas Univ Potsdam Hasso Plattner Inst Potsdam Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9783031343438;9783031343445

The past decade has witnessed significant progress in deploying AI in the medical field. However, most AI models are considered black-boxes, making predictions neither understandable nor interpretable by humans. This limitation is especially significant when they contradict clinicians' expectations based on medical knowledge. This can lead to a lack of trust in the model. In this work, we propose a pipeline to explain AI models. We used a previously devised Neural Network model to present our approach. It predicts the daily risk for patients with heart failure and is a part of a Decision Support System. In our pipeline, we deployed DeepSHAP algorithm to receive global and local explanations. With a global explanation, we defined the most important features in the model and their influence on the prediction. With local explanation, we analyzed individual observations and explained why a specific prediction was made. To validate the clinical relevance of our results, we consulted them with medical experts and made a literature review. Moreover, we described how the proposed pipeline can be integrated into Decision Support Systems. With the above tools, medical personnel can analyze the root of decisions and have insights into how medical parameters should be changed to improve the patient's health state.

关键词： XAI Machine Learning Heart Failure Healthcare

来源：评论

学校读者我要写书评

暂无评论

Optimized Custom Dataset for Efficient Detection of Underwater Trash 1

引用

24th Annual Conference on Towards Autonomous Robotic Systems (TAROS)

作者： Walia, Jaskaran Singh Seemakurthy, Karthik Vellore Inst Technol Chennai India Univ Lincoln Lincoln England

ISBN: (数字)9783031433603

ISBN: (纸本)9783031433597;9783031433603

Accurately quantifying and removing submerged underwater waste plays a crucial role in safeguarding marine life and preserving the environment. While detecting floating and surface debris is relatively straightforward, quantifying submerged waste presents significant challenges due to factors like light refraction, absorption, suspended particles, and color distortion. This paper addresses these challenges by proposing the development of a custom dataset and an efficient detection approach for submerged marine debris. The dataset encompasses diverse underwater environments and incorporates annotations for precise labeling of debris instances. Ultimately, the primary objective of this custom dataset is to enhance the diversity of litter instances and improve their detection accuracy in deep submerged environments by leveraging state-of-the-art deep learning architectures. The source code to replicate the results in this paper can be found at GitHub.

关键词： Deep Learning Computer Vision Visual Object Detection artificial intelligence Robotics Marine Debris Trash Detection Dataset Generation Data Pre-processing

来源：评论

学校读者我要写书评

暂无评论

Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching

Label Shift Quantification with Robustness Guarantees via Di...

引用

5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dussap, Bastien Blanchard, Gilles Cherief-Abdellatif, Badr-Eddine Univ Paris Saclay Lab Math Orsay CNRS Inria Orsay France Univ Paris Cite Sorbonne Univ LPSM CNRS Paris France

ISBN: (纸本)9783031434235;9783031434242

Quantification learning deals with the task of estimating the target label distribution under label shift. In this paper, we first present a unifying framework, distribution feature matching (DFM), that recovers as particular instances various estimators introduced in previous literature. We derive a general performance bound for DFM procedures, improving in several key aspects upon previous bounds derived in particular cases. We then extend this analysis to study robustness of DFM procedures in the misspecified setting under departure from the exact label shift hypothesis, in particular in the case of contamination of the target by an unknown distribution. These theoretical findings are confirmed by a detailed numerical study on simulated and real-world datasets. We also introduce an efficient, scalable and robust version of kernel-based DFM using Random Fourier Features.

关键词： Learning Theory Quantification Kernel Mean Embedding Label Shift Class ratio estimation

来源：评论

学校读者我要写书评

暂无评论

Temporal Graph Based Incident Analysis System for Internet of Things

Temporal Graph Based Incident Analysis System for Internet o...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Yuan, Peng Tang, Lu-An Chen, Haifeng Chang, David S. Sato, Moto Woodward, Kevin NEC Labs Amer Princeton NJ 08540 USA Lockheed Martin Space Denver CO USA

ISBN: (纸本)9783031434297;9783031434303

Internet-of-things (IoTs) deploymassive number of sensors to monitor the system and environment. Anomaly detection on sensor data is an important task for IoT maintenance and operation. In real applications, the occurrence of a system-level incident usually involves hundreds of abnormal sensors, making it impractical for manual verification. The users require an efficient and effective tool to conduct incident analysis and provide critical information such as: (1) identifying the parts that suffered most damages and (2) finding out the ones that cause the incident. Unfortunately, existing methods are inadequate to fulfill these requirements because of the complex sensor relationship and latent anomaly influences in IoTs. To bridge the gap, we design and develop a Temporal Graph based Incident Analysis System (TGIAS) to help users' diagnosis and reaction on reported anomalies. TGIAS trains a temporal graph to represent the anomaly relationship and computes severity ranking and causality score for each sensor. TGIAS provides the list of top k serious sensors and root-causes as output and illustrates the detailed evidence on a graphical view. The system does not need any incident data for training and delivers high accurate analysis results in online time. TGIAS is equipped with a user-friendly interface, making it an effective tool for a broad range of IoTs.

关键词： Internet of things temporal graph anomaly diagnosis causality analysis

来源：评论

学校读者我要写书评

暂无评论

Interlingual Semantic Validation 15th

Interlingual Semantic Validation

引用

15th International Conference on Flexible Query Answering Systems (FQAS)

作者： Gallardo, Carolina Cardenosa, Jesus Baragano, Samantha Univ Politecn Madrid Madrid Spain Dail Software SL Madrid Spain

ISBN: (纸本)9783031429347;9783031429354

The validation of machine translation systems has depended on metrics that compare both form and content of the translated text, where direct human participation was almost indispensable. Within the paradigm of "language pairs" and direct translation, whether rule or statistics-based, the process of evaluating MT systems rely on assessing grammatical correctness and similarity to reference translations. As a counterpart, interlingua-based systems pivot on an intermediate representation of the contents to be translated. In this article, we propose a model for validating the output of automatically translated text in a interlingua-based system, based on the automatization of the semantic validation of source, target and interlingual representation.

关键词： Semantic equivalence Interlingua Graph-based knowledge representation Machine Translation evaluation

来源：评论

学校读者我要写书评

暂无评论

A Case-Based Reasoning Approach to Company Sector Classification Using a Novel Time-Series Case Representation 31st

A Case-Based Reasoning Approach to Company Sector Classifica...

引用

31st International Conference on Case-Based Reasoning Research and Development (ICCBR)

作者： Dolphin, Rian Smyth, Barry Dong, Ruihai Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Coll Dublin Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9783031401763;9783031401770

The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Researchers can access an abundance of time-series data and even modest performance improvements can be translated into significant additional value. In this work, we consider the use of case-based reasoning for an important task in this domain, by using historical stock returns time-series data for industry sector classification. We discuss why time-series data can present some significant representational challenges for conventional case-based reasoning approaches, and in response, we propose a novel representation based on the factorization of a similarity count matrix, which can be readily calculated from raw stock returns data. We argue that this representation is well suited to case-based reasoning and evaluate our approach using a large-scale public dataset for the industry sector classification task, demonstrating substantial performance improvements over several baselines using more conventional representations.

关键词： Case-Based Reasoning Time-Series Finance Representation Learning

来源：评论

学校读者我要写书评

暂无评论

Weighted Multivariate Mean Reversion for Online Portfolio Selection

Weighted Multivariate Mean Reversion for Online Portfolio Se...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Wu, Boqian Lyu, Benmeng Gu, Jiawen Univ Twente Enschede Netherlands Southern Univ Sci & Technol Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783031434235;9783031434242

Portfolio selection is a fundamental task in finance and it is to seek the best allocation of wealth among a basket of assets. Nowadays, Online portfolio selection has received increasing attention from both AI and machine learning communities. Mean reversion is an essential property of stock performance. Hence, most state-of-the-art online portfolio strategies have been built based on this. Though they succeed in specific datasets, most of the existing mean reversion strategies applied the same weights on samples in multiple periods and considered each of the assets separately, ignoring the data noise from short-lived events, trend changing in the time series data, and the dependence of multi-assets. To overcome these limitations, in this paper, we exploit the reversion phenomenon with multivariate robust estimates and propose a novel online portfolio selection strategy named "Weighted Multivariate Mean Reversion" (WMMR) (Code is available at: https://***/boqian333/WMMR).. Empirical studies on various datasets show that WMMR has the ability to overcome the limitations of existing mean reversion algorithms and achieve superior results.

关键词： portfolio selection online learning multivariate robust estimates

来源：评论

学校读者我要写书评

暂无评论

Terminology-Enriched Meta-curriculum Learning for Domain Neural Machine Translation 1

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Chen, Zheng Wang, Yifan Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

As a data-driven approach, neural machine translation (NMT) heavily depends on large parallel corpora. Nevertheless, such corpora are frequently unavailable in domains of user interest, consequently diminishing the performance of NMT models in terms of domain robustness and adaptability. To tackle this challenge, this paper presents an innovative training method for multi-domain translation that leverages meta-curriculum learning and terminology information. To utilize domain-specific terminology, the proposed method first need to extract, align, and filter terms, thereby integrating bilingual terminology into the training dataset. Following this, the aligned sentences are sorted according to the domain similarity scores with the general domain in a curriculum learning manner. Then, the training data is divided into sub-datasets in ascending order of difficulty. A meta-learning technique is then employed to train the model utilizing these partitioned training datasets as tasks, ultimately yielding a translation model with exceptional domain robustness and remarkable domain adaptability. Experimental results on test data from both seen and unseen domains demonstrate that the proposed method yields an average improvement of 2.44 in BLEU scores on the test sets of multiple domains compared to the pre-training and fine-tuning method, and a 1.54 BLEU scores increase over the meta-curriculum learning approach without terminology information. Upon fine-tuning with a small amount of target domain data, the proposed method outperforms these two baselines by 2.62 and 1.5 in BLEU scores, respectively. These outcomes underscore the efficacy of the proposed method in improving NMT performance in scenarios with limited domain-specific data.

关键词： Machine Translation Terminology Curriculum Learning Meta Learning

来源：评论

学校读者我要写书评

暂无评论

Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies 21st

Learning EKG Diagnostic Models with Hierarchical Class Label...

引用

21st International Conference on artificial intelligence in Medicine (AIME)

作者： Wang, Junheng Hauskrecht, Milos Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9783031343438;9783031343445

Electrocardiogram (EKG/ECG) is a key diagnostic tool to assess patient's cardiac condition and is widely used in clinical applications such as patient monitoring, surgery support, and heart medicine research. With recent advances in machine learning (ML) technology there has been a growing interest in the development of models supporting automatic EKG interpretation and diagnosis based on past EKG data. The problem can be modeled as multi-label classification (MLC), where the objective is to learn a function that maps each EKG reading to a vector of diagnostic class labels reflecting the underlying patient condition at different levels of abstraction. In this paper, we propose and investigate an ML model that considers class-label dependency embedded in the hierarchical organization of EKG diagnoses to improve the EKG classification performance. Our model first transforms the EKG signals into a low-dimensional vector, and after that uses the vector to predict different class labels with the help of the conditional tree structured Bayesian network (CTBN) that is able to capture hierarchical dependencies among class variables. We evaluate our model on the publicly available PTB-XL dataset. Our experiments demonstrate that modeling of hierarchical dependencies among class variables improves the diagnostic model performance under multiple classification performance metrics as compared to classification models that predict each class label independently.

关键词： Electrocardiogram Machine Learning Bayesian Network

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500页 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：