2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第4页

Machine Learning for Optimum CT-Prediction for qPCR qPCR最佳ct预测的机器学习

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0103

M. Günay, Evgin Göçeri, R. Balasubramaniyan

引用次数: 10

Efficient Content Replacement in Wireless Content Delivery Network with Cooperative Caching 基于协同缓存的无线内容分发网络中的高效内容替换

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0096

Jihoon Sung, Kyounghye Kim, Junhyuk Kim, J. Rhee

{"title":"Efficient Content Replacement in Wireless Content Delivery Network with Cooperative Caching","authors":"Jihoon Sung, Kyounghye Kim, Junhyuk Kim, J. Rhee","doi":"10.1109/ICMLA.2016.0096","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0096","url":null,"abstract":"Wireless content delivery networks (WCDNs) have received attention as a promising solution to reduce the network congestion caused by rapidly growing demands for mobile content. The amount of reduced congestion is intuitively proportional to the hit ratio in a WCDN. Cooperation among cache servers is strongly required to maximize the hit ratio in a WCDN where each cache server is equipped with a small-size cache storage space. In this paper, we address a content replacement problem that deals with how to manage contents in a limited cache storage space in a reactive manner to cope with a dynamic content demand over time. As a new challenge, we apply reinforcement learning, which is Q-learning, to the content replacement problem in a WCDN with coooperative caching. We model the content replacement problem as a Markov Decision Process (MDP) and finally propose an efficient content replacement strategy to maximize the hit ratio based on a multi-agent Q-learning scheme. Simulation results exhibit that the proposed strategy contributes to achieving better content delivery performance in delay due to a higher hit ratio, compared to typical existing schemes of least recently used (LRU) and least frequently used (LFU).","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127862318","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: 13

Nonlinear Dimensionality Reduction by Unit Ball Embedding (UBE) and Its Application to Image Clustering 单位球嵌入非线性降维及其在图像聚类中的应用

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0177

Behrouz Haji Soleimani, S. Matwin

{"title":"Nonlinear Dimensionality Reduction by Unit Ball Embedding (UBE) and Its Application to Image Clustering","authors":"Behrouz Haji Soleimani, S. Matwin","doi":"10.1109/ICMLA.2016.0177","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0177","url":null,"abstract":"The paper presents an unsupervised nonlinear dimensionality reduction algorithm called Unit Ball Embedding (UBE). Many high-dimensional data, such as object or face images, lie on a union of low-dimensional subspaces which are often called manifolds. The proposed method is able to learn the structure of these manifolds by exploiting the local neighborhood arrangement around each point. It tries to preserve the local structure by minimizing a cost function that measures the discrepancy between similarities of points in the high-dimensional data and similarities of points in the low-dimensional embedding. The cost function is proposed in a way that it provides a hyper-spherical representation of points in the low-dimensional embedding. Visualizations of our method on different datasets show that it creates large gaps between the manifolds and maximizes the separability of them. As a result, it notably improves the quality of unsupervised machine learning tasks (e.g. clustering). UBE is successfully applied on image datasets such as faces, handwritten digits, and objects and the results of clustering on the low-dimensional embedding show significant improvement over existing dimensionality reduction methods.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993519","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: 4

Error Detection of Ocean Depth Series Data with Area Partitioning and Using Sliding Window 基于区域划分和滑动窗口的海洋深度序列数据误差检测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0186

S. Hayashi, S. Ono, S. Hosoda, M. Numao, Ken-ichi Fukui

引用次数: 2

Bee Colony Based Worker Reliability Estimation Algorithm in Microtask Crowdsourcing 微任务众包中基于蜂群的工蚁可靠性估计算法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0127

Alireza Moayedikia, K. Ong, Yee Ling Boo, W. Yeoh

{"title":"Bee Colony Based Worker Reliability Estimation Algorithm in Microtask Crowdsourcing","authors":"Alireza Moayedikia, K. Ong, Yee Ling Boo, W. Yeoh","doi":"10.1109/ICMLA.2016.0127","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0127","url":null,"abstract":"Estimation of worker reliability on microtask crowdsourcing platforms has gained attention from many researchers. On microtask platforms no worker is fully reliable for a task and it is likely that some workers are spammers, in the sense that they provide a random answer to collect the financial reward. Existence of spammers is harmful as they increase the cost of microtasking and will negatively affect the answer aggregation process. Hence, to discriminate spammers and non-spammers one needs to measure worker reliability to predict how likely that a worker put an effort in solving a task. In this paper we introduce a new reliability estimation algorithm works based on bee colony algorithm called REBECO. This algorithm relies on Gaussian process model to estimate reliability of workers dynamically. With bees that go in search of pollen, some are more successful than the others. This maps well to our problem, where some workers (i.e., bees) are more successful than other workers for a given task thus, giving rise to a reliability measure. Answer aggregation with respect to worker reliability rates has been considered as a suitable replacement for conventional majority voting. We compared REBECO with majority voting using two real world datasets. The results indicate that REBECO is able to outperform MV significantly.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186994","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: 8

Classification of X-Ray Galaxy Clusters with Morphological Feature and Tree SVM 基于形态特征和树支持向量机的x射线星系团分类

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0124

Lei Wang, Zhixian Ma, Haiguang Xu, Jie Zhu

引用次数: 1

An LED Based Indoor Localization System Using k-Means Clustering 使用 k-Means 聚类的基于 LED 的室内定位系统

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0048

M. Saadi, Touqeer Ahmad, Yan Zhao, L. Wuttisittikulkij

引用次数: 13

Time Series Classification Using Time Warping Invariant Echo State Networks 基于时间翘曲不变回声状态网络的时间序列分类

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0149

Pattreeya Tanisaro, G. Heidemann

引用次数: 64

Nonlinear Metric Learning for Semi-Supervised Learning via Coherent Point Drifting 基于相干点漂移的半监督学习非线性度量学习

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0058

P. Zhang, Bibo Shi, Charles D. Smith, Jundong Liu

引用次数: 4

Using Domain Knowledge Features for Wind Turbine Diagnostics 利用领域知识特征进行风力涡轮机诊断

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0056

R. Hu, K. Leahy, Ioannis C. Konstantakopoulos, D. Auslander, C. Spanos, A. Agogino

{"title":"Using Domain Knowledge Features for Wind Turbine Diagnostics","authors":"R. Hu, K. Leahy, Ioannis C. Konstantakopoulos, D. Auslander, C. Spanos, A. Agogino","doi":"10.1109/ICMLA.2016.0056","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0056","url":null,"abstract":"Maximising electricity production from wind requires improvement of wind turbine reliability. Component failures result in unscheduled or reactive maintenance on turbines which incurs significant downtime and, in turn, increases production cost, ultimately limiting the competitiveness of renewable energy. Thus, a critical task is the early detection of faults. To this end, we present a framework for fault detection using machine learning that uses Supervisory Control and Data Acquisition (SCADA) data from a large 3MW turbine, supplemented with features derived from this data that encapsulate expert knowledge about wind turbines. These new features are created using application domain knowledge that is general to large horizontal-axis wind turbines, including knowledge of the physical quantities measured by sensors, the approximate locations of the sensors, the time series behaviour of the system, and some statistics related to the interpretation of sensor measurements. We then use mRMR feature selection to select the most important of these features. The new feature set is used to train a support vector machine to detect faults. The classification performance using the new feature set is compared to performance using the original feature set. Use of the new feature set achieves an F1-score of 90%, an improvement of 27% compared to the original feature set.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131892974","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: 20