2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第7页

Subspace Clustering and Feature Extraction Based on Latent Sparse Low-Rank Representation 基于潜在稀疏低秩表示的子空间聚类与特征提取

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949212

Lina Zhao, Fang Ma, Hongwei Yang

引用次数: 1

Comments on “Finite-Time Analysis of the Multiarmed Bandit Problem” 评“多武装土匪问题的有限时间分析”

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949232

Lu-ning Zhang, Xin Zuo, Jian-wei Liu, Weimin Li, Nobuyasu Ito

引用次数: 3

Exploring the LIMB Position Effect on Wearable-Ultrasound-Based Gesture Recognition 肢体位置对可穿戴超声手势识别的影响研究

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949209

Xingchen Yang, J. Yan, Yi-Zhao, Honghai Liu

引用次数: 0

Analyzing Causal Relationships of Sensor Data and Infrared Images to Stabilize Garbage Power Generation 分析传感器数据与红外图像的因果关系，稳定垃圾发电

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949308

K. Matsubayashi, T. Anjiki, Shunji Maeda

引用次数: 0

Multi-Task Learning With Localized Generalization Error Model 基于局部泛化误差模型的多任务学习

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949255

Wendi Li, Yi Zhu, Ting Wang, Wing W. Y. Ng

{"title":"Multi-Task Learning With Localized Generalization Error Model","authors":"Wendi Li, Yi Zhu, Ting Wang, Wing W. Y. Ng","doi":"10.1109/ICMLC48188.2019.8949255","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949255","url":null,"abstract":"In cases, the same or similar network architecture is used to deal with related but different tasks, where tasks come from different statistical distributions in the sample input space and share some common features. Multi-Task Learning (MTL) combines multiple related tasks for training at the same time, so as to learn some shared feature representation among multiple tasks. However, it is difficult to improve each task when statistical distributions of these related tasks are greatly different. This is caused by the difficulty of extracting an effective generalization of feature representation from multiple tasks. Moreover, it also slows down the convergence rate of MTL. Therefore, we propose a MTL method based on the Localized Generalization Error Model (L-GEM). The L-GEM improves the generalization capability of the trained model by minimizing the upper bound of generalization error of it with respect to unseen samples similar to training samples. It also helps to narrow the gap between different tasks due to different statistical distributions in MTL. Experimental results show that the L-GEM speeds up the training process while significantly improves the final convergence results.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"601 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134063374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supervised Link Prediction in Co-Authorship Networks Based on Research Performance and Similarity of Research Interests and Affiliations 基于研究绩效和研究兴趣与隶属关系相似性的合作作者网络监督链接预测

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949320

D. Hassan

{"title":"Supervised Link Prediction in Co-Authorship Networks Based on Research Performance and Similarity of Research Interests and Affiliations","authors":"D. Hassan","doi":"10.1109/ICMLC48188.2019.8949320","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949320","url":null,"abstract":"Predicting the emergence of future research collaborations between authors in academic social network is a very effective example that demonstrates the link prediction problem. This problem refers to predicting the potential existence or absence of link between a pair of nodes in social networks (SN). Since the majority of previous research work on link prediction only considered predictor variables (i.e., features) extracted from SN structure, this paper aims to investigate the impact of using other types of predictor variables on solving link prediction problem in co-authorship network. It proposes a new method for supervised link prediction in co-authorship networks using predictors extracted by: computing the similarity between the research interests of each two author nodes in the network, the similarity between their affiliations, the sum of their research performance indices as well as the similarity between the two author nodes themselves. The preliminary results of our approach show that the sum of research performance indices of two author nodes has the most impact on the performance of supervised link prediction which motivates us to do further analysis on using such a predictor.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"707 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132947416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Microplastics Using Machine Learning 使用机器学习检测微塑料

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949221

Z. Chaczko, Peter Wajs-Chaczko, David Tien, Y. Haidar

引用次数: 11

Developing the Interpretability of Deep Artificial Neural Network on Application Problems 开发深度人工神经网络在应用问题上的可解释性

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949230

Sheng-An Yang, Meng-Han Yang

{"title":"Developing the Interpretability of Deep Artificial Neural Network on Application Problems","authors":"Sheng-An Yang, Meng-Han Yang","doi":"10.1109/ICMLC48188.2019.8949230","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949230","url":null,"abstract":"In recent years, the use of electronic health records (EHR) has increased dramatically. Mining hidden knowledge in “big data” from EHR has become a subject worthy of exploration. On the other hand, many recent applications used deep artificial neural network (ANN) to analyze EHR data and yielded great performance. Accordingly, this study developed functional models using deep ANN, and tried to validate effectiveness of this method in regression analysis and classification problem. Based on datasets downloaded from the UC Irvine Machine Learning Repository, the output mean squared error value 0.840 was within the range of one variance for the regression analysis. Similarly, the prediction accuracy 73.0% on the testing data was reported for the classification problem. Another focus of this study was identifying critical attributes using the layer-wise relevance propagation (LRP) algorithm to improve interpretability of deep ANN. According to evaluation outcomes, the identified features would match with those recognized by univariate analysis. In summary, effectiveness of deep ANN and LRP on application problems has been validated in this study.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125102898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression 基于分类驱动回归的两阶段训练的溶解氧状态数值预测

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949196

Pengfei Guo, Han Liu, Shuangyin Liu, Longqin Xu

{"title":"Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression","authors":"Pengfei Guo, Han Liu, Shuangyin Liu, Longqin Xu","doi":"10.1109/ICMLC48188.2019.8949196","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949196","url":null,"abstract":"Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at numerical prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned numerical prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards numerical prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the numerical prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Exploring and Evaluating the Scalability and Eficinecy of Apache Spark Using Educational Datasets 利用教育数据集探索和评估Apache Spark的可扩展性和效率

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949260

Jian Zhang, Zijiang Yang, Y. Benslimane

引用次数: 1