检索结果-内蒙古大学图书馆

15th ACM international conference on Web Search and data Mining (WSDM)

作者： Wang, Chao Liu, Jingping Zhuang, Tianyi Li, Jiahang Liu, Juntao Xiao, Yanghua Wang, Wei Xie, Rui Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China Fudan Aishu Cognit Intelligence Joint Res Ctr Shanghai Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Meituan Shanghai Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781450391320

Abbreviations often used in our daily communication play an important role in natural language processing. Most of the existing studies regard the Chinese abbreviation prediction as a sequence labeling problem. However, sequence labeling models usually ignore label dependencies in the process of abbreviation prediction, and the label prediction of each character should be conditioned on its previous labels. In this paper, we propose to formalize the Chinese abbreviation prediction task as a sequence generation problem, and a novel sequence-to-sequence model is designed. To boost the performance of our deep model, we further propose a multi-level pre-trained model that incorporates character, word, and concept-level embeddings. To evaluate our methods, a new dataset for Chinese abbreviation prediction is automatically built, which contains 81,351 pairs of full forms and abbreviations. Finally, we conduct extensive experiments on a public dataset and the built dataset, and the experimental results on both datasets show that our model outperforms the state-of-the-art methods. More importantly, we build a large-scale database for a specific domain, i.e., life services in Meituan Inc., with high accuracy of about 82.7%, which contains 4,134,142 pairs of full forms and abbreviations. The online A/B testing on Meituan APP and Dianping APP suggests that Click-Through Rate increases by 0.59% and 0.86% respectively when the built database is used in the searching system. We have released our API on http://***/ddemos/abbr/ with over 87k API calls in 9 months.

关键词： Chinese abbreviation Sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

Feature Selection algorithms and Student Academic Performance: A Study 3rd

Feature Selection Algorithms and Student Academic Performanc...

引用

3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Jalota, Chitra Agrawal, Rashmi Faculty of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9789811551123

In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Consensus Variational and Monte Carlo algorithms for Bayesian Nonparametric Clustering 8

Consensus Variational and Monte Carlo Algorithms for Bayesia...

引用

8th IEEE international conference on Big data (Big data)

作者： Ni, Yang Jones, David Wang, Zeya Texas A&M Univ Dept Stat College Stn TX 77843 USA Petuum Inc Pittsburgh PA USA

ISBN: (纸本)9781728162515

We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.

关键词： big data embarrassingly parallel computing unsupervised learning image clustering

来源：评论

学校读者我要写书评

暂无评论

On-manifold Adversarial data Augmentation Improves Uncertainty Calibration 25

On-manifold Adversarial Data Augmentation Improves Uncertain...

引用

25th international conference on Pattern Recognition (ICPR)

作者： Patel, Kanil Beluch, William Zhang, Dan Pfeiffer, Michael Yang, Bin Bosch Ctr Artificial Intelligence Renningen Germany Univ Stuttgart Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9781728188089

Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks. To improve uncertainty estimation, we propose On-Manifold Adversarial data Augmentation or OMADA, which specifically attempts to generate challenging examples by following an on-manifold adversarial attack path in the latent space of an autoencoder that closely approximates the decision boundaries between classes. On a variety of datasets and for multiple network architectures, OMADA consistently yields more accurate and better calibrated classifiers than baseline models, and outperforms competing approaches such as Mixup, as well as achieving similar performance to (at times better than) post-processing calibration methods such as temperature scaling. Variants of OMADA can employ different sampling schemes for ambiguous on-manifold examples based on the entropy of their estimated soft labels, which exhibit specific strengths for generalization, calibration of predicted uncertainty, or detection of out-of-distribution inputs.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Detection of Cyberbullying on Twitter data Using Machine Learning 6th

Detection of Cyberbullying on Twitter Data Using Machine Lea...

引用

6th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Sandesh, A. Asha, H.V. Supriya, P. Faculty Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811613418

In later half of the twentieth century, though digital revolution began, social communications evolved within small cultural boundaries, their progress was bound by geo-spatial limitations of traditional communications. With the advent of information communication technologies (ICT), new innovations have transcended the spatial limitations, revolutionizing social networking, and world is becoming smaller, borderless, and a better place. However, together with advancements comes the evil effect of technology. The term Social Media (SM) has taken over our lives globally. Social platforms have become a part of our daily affairs, and the increasing use of these platforms by increasing number of users is generating large amount of user behaviour related data every day. With popularity of social platforms, it has introduced a new form of individual violence behaviour termed as cyberbullying. Cyberbullying has had an adverse effect on human’s life creating severe problems, and at times, individuals have been victimized to attempt suicide. This creates the need for construction of models to detect cyberbullying to safeguard the interest of individuals. This paper provides insights about cyberbullying and the process of detecting cyberbullying using machine learning algorithms. The proposed system extracts the information related to network, users, and tweet contents from Twitter platform. The dataset considered for experimenting includes labelled data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Colorectal Cancer Epidemiology, Screening and Segmentation using CNNs

Colorectal Cancer Epidemiology, Screening and Segmentation u...

引用

New Frontiers in Communication, Automation, Management and Security (ICCAMS), international conference on

作者： Mn Prashanth S SenthilKumar Dept. of CSE SOCSE Presidency University Bengaluru India SOCSE Presidency University Bengaluru India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Third most frequent diagnosed cancer according to Globocon 2020 across World, both sexes, and of all ages is Colorectal cancer (CRC)][1]. Considering mortality CRC is second and ranks third in incidence for cancer across sexes and ages according to Globocon 2020. GLOBOCAN 2020 reports that incidence of CRC stated globally are 19292789 and considering India it is 31646 and mortality due to the disease is reported worldwide are 9958133 and for India are 19236. The cancer problem can be lowered by accepting healthy lifestyle and diet, avoiding smoking, ensuring physical activity. And regular screening for early detection so as to obtain quality treatment at early stages. CRC Screening is accomplished predominantly with computed tomography colonography (CTC). Various Machine Learning (ML) algorithms are adopted to detect, classify and risk prediction of the malignancy. The Proposed Segmentation model, deep adversarial model based on a complete convolutional network provides, with current experimentation provides IoU of 0.9471, DSC of 0.9725, Accuracy of about 0.9928, a Sensitivity of about 0.9774, and a Specificity of about 0.9954.

关键词： Surveys Sensitivity Virtual colonoscopy Computational modeling Machine learning data models Recording

来源：评论

学校读者我要写书评

暂无评论

The Role of the Essential Manifold in data Mining – An Introductory Approach 23rd

The Role of the Essential Manifold in Data Mining – An I...

引用

23rd international conference on Computational Science and Its Applications, ICCSA 2023

作者： de Fátima Pina, Maria Andrade, Marina Alexandra ISCTE - IUL University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal ISLA - Santarém Superior Institute of Management and Administration of Santarém Rua Teixeira Guedes 31 Santarém2000-029 Portugal ISR - UC Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Rua Silvio Lima - Pólo II Coimbra3030-290 Portugal ISTAR - IUL Information Sciences and Technologies and Architecture Research Center University Institute of Lisbon Av. das Forças Armadas Lisbon1649-026 Portugal

ISBN: (纸本)9783031371288

Interpolating data and the application of data mining techniques in nonlinear manifolds plays a significant role in different areas of knowledge, ranging from computer vision and robotics, to industrial and medical requests, and these growing number of applications have sparked the research interest of the scientific community to these topics. The Generalized Essential manifold, briefly, Essential manifold, consisting of the product of the Grassmann manifold of all k-dimensional subspaces of Rn and the Lie group of rotations in Rn, for instance, plays an important role in the problem of recovering the structure and motion from a sequence of images, also known as stereo matching, which is a crucial problem in image processing and computer vision. A well-known recursive procedure to generate interpolating polynomial curves in Euclidean spaces is the classical De Casteljau algorithm, which is a simple and powerful tool widely used in the field of Computer Aided Geometric Design, particularly because it is essentially geometrically based. This algorithm has been generalized to geodesically complete Riemannian manifolds. Thus, having this in mind, in this work we present all the ingredients for a detailed implementation of the generalized De Casteljau algorithm to generate geometric cubic polynomials in the Essential manifold preparing the ground to solve different real interpolation problems in this manifold. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

The efficiency of the Temporal Medical data Retrieval

The efficiency of the Temporal Medical Data Retrieval

引用

2021 international conference on Information and Digital Technologies, IDT 2021

作者： Kvet, Michal Matiasko, Karol University of Žilina Faculty of Management Science and Informatics Department of Informatics Žilina Slovakia

ISBN: (纸本)9781665436922

One of the significant aspects influencing patient treatment is associated with decision-making correctness based on reliable data inputs. data should be managed, treated, and provided in a robust and performance-effective manner. Commonly, the patient needs to be monitored over time reflecting the evolution. This paper deals with the temporal database models and proposes an effective solution for the data evaluation during the retrieval, so the query request can end significantly sooner. It also points to the current problem of data management in the cloud technology as a central data repository, in which the data time perspectives can be shifted reflecting the time zone, so the results must be transformed to provide relevant data output in a client site. © 2021 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

An Efficient Tool for Semantic Biomedical Document Analysis 16th

An Efficient Tool for Semantic Biomedical Document Analysis

引用

16th international conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Li, Meijing Hu, Jingyu Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789813367562

With the rapid growth of biomedical documents, finding out interested documents efficiently becomes a challenging for researchers. To improve the efficiency and display biomedical information in a more direct way, a medical knowledge graph-based semantic text analysis tool is developed. This tool is based on improved bag-of-words and ontology-based semantic text mining algorithms, supporting visualized biomedical conception and documents analysis. The testing results show that proposed models perform well on medical documents clustering, accuracy in different dataset is above 82% and the best one reaches 96%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

引用

MACHINE LEARNING 2021年第8期110卷 2131-2150页

作者： Jiang, Haiyan Xiong, Haoyi Wu, Dongrui Liu, Ji Dou, Dejing Baidu Res Big Data Lab Beijing Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size setting, one may prefer modified principal components, with penalized loadings, and automated penalty selection by implementing model selection among these different models with varying penalties. The earlier work (Zou et al. in J Comput Graph Stat 15(2):265-286, 2006;Gaynanova et al. in J Comput Graph Stat 26(2):379-387, 2017) has proposed penalized PCA, indicating the feasibility of model selection in l(2)-penalized PCA through the solution path of Ridge regression, however, it is extremely time-consuming because of the intensive calculation of matrix inverse. In this paper, we propose a fast model selection method for penalized PCA, named approximated gradient flow (AgFlow), which lowers the computation complexity through incorporating the implicit regularization effect introduced by (stochastic) gradient flow (Ali et al. in: The 22nd international conference on artificial intelligence and statistics, pp 1370-1378, 2019;Ali et al. in: international conference on machine learning, 2020) and obtains the complete solution path of l(2)-penalized PCA under varying l(2)-regularization. We perform extensive experiments on real-world datasets. AgFlow outperforms existing methods (Oja and Karhunen in J Math Anal Appl 106(1):69-84, 1985;Hardt and Price in: Advances in neural information processing systems, pp 2861-2869, 2014;Shamir in: international conference on machine learning, pp 144-152, PMLR, 2015;and the vanilla Ridge estimators) in terms of computation costs.

关键词： Model selection Gradient flow Implicit regularization Penalized PCA Ridge

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：