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

1,926 篇 会议
13 篇 期刊文献
10 册 图书

馆藏范围

1,949 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,033 篇 工学
- 909 篇 计算机科学与技术...
- 491 篇 软件工程
- 297 篇 控制科学与工程
- 122 篇 信息与通信工程
- 116 篇 电气工程
- 73 篇 生物工程
- 44 篇 生物医学工程（可授...
- 40 篇 仪器科学与技术
- 34 篇 机械工程
- 30 篇 电子科学与技术（可...
- 21 篇 化学工程与技术
- 21 篇 网络空间安全
- 13 篇 光学工程
- 9 篇 动力工程及工程热...
321 篇 理学
- 211 篇 数学
- 91 篇 生物学
- 82 篇 统计学（可授理学、...
- 49 篇 系统科学
- 28 篇 物理学
- 17 篇 化学
287 篇 管理学
- 218 篇 管理科学与工程(可...
- 95 篇 图书情报与档案管...
- 32 篇 工商管理
35 篇 医学
- 30 篇 临床医学
- 28 篇 基础医学(可授医学...
- 21 篇 药学(可授医学、理...
21 篇 法学
- 20 篇 社会学
18 篇 经济学
- 18 篇 应用经济学
11 篇 农学
- 11 篇 作物学
7 篇 教育学
- 7 篇 教育学
6 篇 军事学
5 篇 文学
2 篇 艺术学
1 篇 哲学

主题

493 篇 machine learning
165 篇 data mining
155 篇 computer science
153 篇 machine learning...
141 篇 support vector m...
118 篇 cybernetics
105 篇 neural networks
94 篇 clustering algor...
90 篇 computer network...
88 篇 learning systems
80 篇 educational inst...
80 篇 distributed comp...
66 篇 electronic mail
66 篇 artificial intel...
64 篇 support vector m...
62 篇 algorithm design...
60 篇 humans
59 篇 pattern recognit...
58 篇 computational mo...
58 篇 testing

机构

63 篇 department of co...
40 篇 department of co...
24 篇 school of comput...
23 篇 department of co...
14 篇 machine learning...
14 篇 department of co...
14 篇 college of mecha...
13 篇 school of comput...
12 篇 college of compu...
12 篇 institute of com...
11 篇 hebei univ coll ...
8 篇 institute of com...
8 篇 school of comput...
7 篇 department of in...
7 篇 school of comput...
7 篇 department of co...
7 篇 department of au...
7 篇 department of in...
6 篇 key laboratory o...
6 篇 key laboratory o...

作者

26 篇 yeung daniel s.
22 篇 d.s. yeung
16 篇 xi-zhao wang
12 篇 hui wang
12 篇 ng wing w. y.
12 篇 tsang eric c. c.
11 篇 e.c.c. tsang
11 篇 daniel s. yeung
11 篇 kuan-quan wang
10 篇 wang xi-zhao
10 篇 yeung
10 篇 tsang
9 篇 jane you
9 篇 you jane
9 篇 zhang david
8 篇 qing he
8 篇 de-gang chen
7 篇 qing-hua li
7 篇 yu zhiwen
7 篇 li le

语言

1,945 篇 英文
3 篇 中文
1 篇 其他

检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

共 1949 条记录，以下是131-140 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：

Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

Optimal Minimum Energy Path Planning of Mobile Robot Based o...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Jiang Du Zhou Da-Lin Li Gong-Fa Lu Bao-Li Yun Jun-Tong Liu Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Computing University of Portsmouth Portsmouth UK Hubei key laboratory of hydroelectric machinery design & maintenance China Three Gorges University Yichang China Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan University of Science and Technology Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan China Institute of Semiconductors Chinese Academy of Sciences Beijing China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Neural Network Approaches for Long Document Classification 2022

Hierarchical Neural Network Approaches for Long Document Cla...

引用

14th international conference on machine learning and computing, icmlc 2022

作者： Khandve, Snehal Ishwar Wagh, Vedangi Kishor Wani, Apurva Dinesh Joshi, Isha Mandar Joshi, Raviraj Bhuminand Computer Engineering Department Pune Institute of Computer Technology India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781450395700

Text classification algorithms investigate the intricate relationships between words or phrases and attempt to deduce the document's interpretation. In the last few years, these algorithms have progressed tremendously. Transformer architecture and sentence encoders have proven to give superior results on natural language processing tasks. But a major limitation of these architectures is their applicability for text no longer than a few hundred words. In this paper, we explore hierarchical transfer learning approaches for long document classification. We employ pre-trained Universal Sentence Encoder (USE) and Bidirectional Encoder Representations from Transformers (BERT) in a hierarchical setup to capture better representations efficiently. Our proposed models are conceptually simple where we divide the input data into chunks and then pass this through base models of BERT and USE. Then output representation for each chunk is then propagated through a shallow neural network comprising of LSTMs or CNNs for classifying the text data. These extensions are evaluated on 6 benchmark datasets. We show that USE + CNN/LSTM performs better than its stand-alone baseline. Whereas the BERT + CNN/LSTM performs on par with its stand-alone counterpart. However, the hierarchical BERT models are still desirable as it avoids the quadratic complexity of the attention mechanism in BERT. Along with the hierarchical approaches, this work also provides a comparison of different deep learning algorithms like USE, BERT, HAN, Longformer, and BigBird for long document classification. The Longformer approach consistently performs well on most of the datasets. © 2022 ACM.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

DeepGCNMIL: Multi-head Attention Guided Multi-Instance learning Approach for Whole-Slide Images Survival Analysis Using Graph Convolutional Networks 2022

DeepGCNMIL: Multi-head Attention Guided Multi-Instance Learn...

引用

14th international conference on machine learning and computing, icmlc 2022

作者： Wu, Fei Liu, Pei Fu, Bo Ye, Feng School of Computer Science and Engineering University of Electronic Science and Technology of China China Institute of Clinical Pathology Sichuan University China

ISBN: (纸本)9781450395700

Analyzing giga-pixel Whole-Slide Images (WSIs) has difficulty in expanding to large-scale data set due to labor intensive patchlevel annotation. Current multi-instance learning (MIL) frameworks guided by attention mechanism have successfully built the relation between giga-pixel WSI and survival, which is suitable for large-scale data analysis. However, the simple aggregation of patchlevel features may not comprehensively characterize WSI, since it ignores the internal connection between patches. To address this problem, this paper proposes a graph convolutional networks-based MIL framework, named as DeepGCNMIL. We firstly cluster patches into several phenotypes based on their similarity, then build graphs for these clusters to consider internal connections among patches through node edges and exploit a three-layer graph convolutional network (GCN) to learn representation of each phenotype. Moreover, we introduce multi-head attention to aggregate phenotype features into WSI representation for prognostic risk assessment. Our method achieves a C-index of 0.673 (± 0.053) on the NLST dataset (0.035 ahead of the second place) and 0.632 (± 0.065) on the TCGA_BRCA dataset (0.018 ahead of the second place), which show that for large-scale prognostic modeling of Giga-pixel digital pathological images, our method outperforms similar WSI survival prediction models. This novel MIL framework could be effectively utilized to assess the prognosis risk of individual patients and help provide personalized medicine. © 2022 Association for computing machinery. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Multi-step Point-of-Interest-level Crowd Flow Prediction Based on Meta learning 2022

Multi-step Point-of-Interest-level Crowd Flow Prediction Bas...

引用

14th international conference on machine learning and computing, icmlc 2022

作者： Feng, Yuting Zhu, Xinning Tang, Xiaosheng Hu, Zheng Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology China

ISBN: (纸本)9781450395700

Point-of-Interest-level(POI) crowd flow prediction is an important task for businesses and consumers. Based on POI-level crowd flow prediction, businesses could make more reasonable business arrangements, and consumers could make more suitable travel plans. However, POI-level crowd flow prediction is a challenging task for two aspects: 1) Compared with region-level crowd flow, the area of POI is smaller and the fluctuation of POI-level crowd flow is greater;2) There are diverse temporal correlations of different POIs and varies over time. To tackle the above challenges, following the antoencoder architecture, we propose a multi-step POI-level crowd flow prediction model(Ms-PLCFP) to predict the crowd flow at all POIs at once. A meta learner is used to obtain meta knowledge from POI category, POI popularity, etc. Then meta-RNN+ is applied to model the relations between temporal correlations and meta knowledge so as to capture diverse temporal correlations. Furthermore, a multi-scale temporal attention mechanism which contains multiple different scales of temporal attention is employed to smooth input crowd flow at lower level and capture global dependencies of input crowd flow at higher level. We evaluated Ms-PLCFP on two real-world datasets and Ms-PLCFP achieved significant improvements over the baselines, which shows the effectiveness of our model. © 2022 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Effectiveness of Transfer learning in Autonomous Driving using Model Car 2021

Effectiveness of Transfer Learning in Autonomous Driving usi...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Chiba, Shohei Sasaoka, Hisayuki Asahikawa Japan Asahikawa Japan

ISBN: (纸本)9781450389310

We have known that reinforcement learning, deep learning, and deep reinforcement learning effectively acquire action rules for the autonomous motion of objects. However, it is known that these learning processes require a large amount of learning time. Besides, we should consider the similarity of the environment between the training target and the test target. In actual autonomous driving, there is no such thing as driving only on a course that has been learned in advance. In this study, the autonomous driving of a model car is used as the experimental object. The training target for acquiring the learning model and the actual driving courses are changed. In this study, we report on the effectiveness of transfer learning using a model car as the basis for a learning model acquired by reinforcement learning. © 2021 ACM.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Ensemble learning in Stock Market Prediction 2021

Ensemble Learning in Stock Market Prediction

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Ezzeddine, Hassan R. Achkar, Roger Department of Computer and Communications Engineering Faculty of Engineering American University of Science and Technology Beirut Lebanon Lebanon

ISBN: (纸本)9781450389310

In recent years, the increasing influence of machine learning in different industries had inspired many traders to benefit from it in the world of finance, stock trading is one of the most important activities. Predicting the direction of stock prices is a widely studied subject in many fields including trading, finance, statistics and computer science. The main concern for Investors is to maximize their profit if they determine when to buy/sell an investment they apply Analytical methods that makes use of different sources ranging from news to price data, all aiming at predicting the company's future stock price ML applications have presented investors with something new. A combination of technologies that could entirely reshape the way they make investment decisions. The purpose of this thesis is to leverage the aggregation of technical, fundamental, and sentiment analysis with stacked machine learning models capable of predicting profitable actions to be executed. © 2021 ACM.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

DSP-PIGAN: A Precision-Consistency machine learning Algorithm for Solving Partial Differential Equations 2021

DSP-PIGAN: A Precision-Consistency Machine Learning Algorith...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Wang, Yunzhuo Sun, Hao Sun, Guangzhong University of Science and Technology of China Hefei China

ISBN: (纸本)9781450389310

Partial differential equations (PDEs) are the most ubiquitous tool for modeling problems in nature. In recent years, machine learning techniques are adopted to solve PDEs. However, the prediction errors of existing machine learning methods vary widely on different subdomains of PDEs. How to achieve precision-consistency is a crucial and complex issue for machine learning methods for solving PDEs. To tackle this issue, we propose DSP, an adaptive framework for solving PDEs. DSP is composed of domain decomposition, searching for singular subdomains, and prediction. Furthermore, a novel generative model, physics-informed generative adversarial network (PIGAN), is designed to solve PDEs. In addition, we introduce points with high-precision labels into the training process of the model to improve model accuracy. We test the effectiveness of our approach on three real physical equations: Poisson equation, Helmhotz equation and Eikonal equation. Through experiments, we prove that the combination of DSP and PIGAN outperforms various state-of-the-art baselines. © 2021 ACM.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques 23

Optimizing Tensor Computations: From Applications to Compila...

引用

2023 ACM/SIGMOD international conference on Management of Data, SIGMOD 2023

作者： Boehm, Matthias Interlandi, Matteo Jermaine, Chris TU Berlin Berlin Germany Mircosoft GSL Los AngelesCA United States Rice University HoustonTX United States

ISBN: (纸本)9781450395076

machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges. © 2023 ACM.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Bird Songs Recognition Based on Ensemble Extreme learning machine 2021

Bird Songs Recognition Based on Ensemble Extreme Learning Ma...

引用

2021 13th international conference on machine learning and computing, icmlc 2021

作者： Xie, Shanshan Xu, Haifeng Liu, Jiang Zhang, Yan Lv, Danjv College of Big Data and Intelligence Engineering Southwest Forestry University China

ISBN: (纸本)9781450389310

ELM (Extreme learning machine) is a random method for Single-hidden layer feedforward neural network construction, and MFCC (Mel-frequency Cepstrum Coefficient) is a kind of feature parameter for speech recognition. Based on Ensemble ELM research on bird songs recognition technology, this paper firstly preprocesses the bird songs data collected by web crawler, then extracts MFCC feature parameters from the songs data, and gets the improved MFCC feature parameters through differential calculation. Finally, Ensemble ELM is used for bird songs classification and recognition. The experimental results show that the Ensemble ELM method can achieve a recognition rate of 90.42% in the classification of 10 kinds of birds. © 2021 ACM.

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Univariate and Multivariate Time Series Forecasting for CPO Prices Using machine learning Techniques 8th

A Comparative Study of Univariate and Multivariate Time Seri...

引用

8th international Visual Informatics conference, IVIC 2023

作者： Mohd Fuad, Juz Nur Fatiha Deena Ibrahim, Zaidah Adam, Noor Latiffah Mat Diah, Norizan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9789819973385

The Malaysian palm oil sector has significantly contributed to developing the domestic economy and the global palm oil market. However, the fluctuation in Crude Palm Oil (CPO) prices poses a significant risk to farmers, producers, traders, consumers, and others involved in CPO production and marketing. An accurate CPO price forecasting technique is required to aid decision-making in risky and unpredictable scenarios. Hence, this project aims to compare the performances of four-time series forecasting models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM, in the context of univariate and multivariate analysis for CPO prices in Malaysia. This research methodology is based on five phases: research understanding, data understanding, data preparation, modeling, and evaluation. Monthly CPO prices, the production and export volume of CPO, selected vegetable oil prices, crude oil prices, and monthly exchange rate data from January 2009 to December 2022 were utilized. The metrics evaluation of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were then performed to compare and evaluate the performance of the models. Experimental analysis indicates that the CNN model trained on a multivariate dataset with carefully selected significant independent variables outperformed other models. With a configuration of 500 epochs and early stopping, it achieved remarkable results compared to models trained using a univariate approach, boasting an RMSE of 245.611 and a MAPE of 7.13. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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195页 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：