2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

Multiple Kernel Learning Using Sparse Representation 基于稀疏表示的多核学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-79

N. Klausner, M. Azimi-Sadjadi

引用次数: 1

Medicare Fraud Detection Using Machine Learning Methods 使用机器学习方法检测医疗保险欺诈

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-48

Richard A. Bauder, T. Khoshgoftaar

引用次数: 60

Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network 基于ResNet-50深度神经网络迁移学习的恶意软件分类

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-19

Edmar R. S. Rezende, Guilherme C. S. Ruppert, T. Carvalho, F. Ramos, Paulo Lício de Geus

引用次数: 179

An Ensembled RBF Extreme Learning Machine to Forecast Road Surface Temperature 一种集成RBF极限学习机预测路面温度

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-26

Bo Liu, Shuo Yan, Huanling You, Yan Dong, Jianqiang Li, Yong Li, Jianlei Lang, Rentao Gu

{"title":"An Ensembled RBF Extreme Learning Machine to Forecast Road Surface Temperature","authors":"Bo Liu, Shuo Yan, Huanling You, Yan Dong, Jianqiang Li, Yong Li, Jianlei Lang, Rentao Gu","doi":"10.1109/ICMLA.2017.00-26","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-26","url":null,"abstract":"At present, high road surface temperature (RST) is threatening the safety of expressway transportation. It can lead to accidents and damages to road, accordingly, people have paid more attention to RST forecasting. Numerical methods on RST prediction are often hard to obtain precise parameters, whereas statistical methods cannot achieve desired accuracy. To address these problems, this paper proposes GBELM-RBF method that utilizes gradient boosting to ensemble Radial Basis Function Extreme Learning Machine. To evaluate the performance of the proposed method, GBELM-RBF is compared with other ELM algorithms on the datasets of airport expressway and Badaling expressway during November 2012 and September 2014. The root mean squared error (RMSE), accuracy and Pearson Correlation Coefficient (PCC) of these methods are analyzed. The experimental results show that GBELM-RBF has the best performance. For airport expressway dataset, the RMSE is less than 3, the accuracy is 78.8% and PCC is 0.94. For Badaling expressway dataset, the RMSE is less than 3, the accuracy is 81.2% and PCC is 0.921.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"65 1","pages":"977-980"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87002288","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: 5

Unsupervised Anomaly Detection for Digital Radio Frequency Transmissions 数字射频传输的无监督异常检测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-54

Michael Walton, M. Ayache, Logan Straatemeier, Daniel Gebhardt, Benjamin Migliori

{"title":"Unsupervised Anomaly Detection for Digital Radio Frequency Transmissions","authors":"Michael Walton, M. Ayache, Logan Straatemeier, Daniel Gebhardt, Benjamin Migliori","doi":"10.1109/ICMLA.2017.00-54","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-54","url":null,"abstract":"We present a novel method of unsupervised anomaly detection using long-short-term memory mixture density networks (LSTM-MDN), applied to timeseries data of digital radio transmissions. The modern radio frequency (RF) environment is a dynamic and ever-changing complex milieu of signals, environmental effects, unintentional interference, and intentional jamming. A consequence of this complex mix is that RF receivers must become better and better at rejecting anomalous signals in order to recover the transmitted information. However, it is not always possible to know a priori what constitutes a valid signal and what constitutes an anomaly (intentional or otherwise), especially with the adoption of cognitive radio techniques. We show that an LSTM-MDN model is able to rapidly learn the training set and produce probability distribution functions for the expected signal as a function of time. We then demonstrate that the negative log likelihood of an incoming test transmission, conditioned on the training set, provides a metric that allows anomalous signals to be detected and labeled. We demonstrate this method for eight popular modulations and for three different anomaly types. By applying unsupervised learning in the temporal domain, we report a fully-generalizable anomaly detection method that may be applied to signals for which the transmission parameters may be unknown or obscured.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"28 1","pages":"826-832"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86145491","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

RBF-FIRMLP Architecture for Digit Recognition 数字识别的RBF-FIRMLP体系结构

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-125

Cristinel Codrescu

引用次数: 1

Anytime Exploitation of Stragglers in Synchronous Stochastic Gradient Descent 同步随机梯度下降中离散机的随时开发

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-166

Nuwan S. Ferdinand, Benjamin Gharachorloo, S. Draper

引用次数: 21

An Exploratory Study of Oral and Dental Health in Canada 加拿大口腔和牙齿健康的探索性研究

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00010

Andrei Belcin, Sean L. A. Floyd, A. Asiri, H. Viktor

引用次数: 0

An Empirical Study of Cross-Lingual Transfer Learning Techniques for Small-Footprint Keyword Spotting 跨语言迁移学习技术在小足迹关键词识别中的实证研究

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-150

Ming Sun, A. Schwarz, Minhua Wu, N. Strom, S. Matsoukas, S. Vitaladevuni

{"title":"An Empirical Study of Cross-Lingual Transfer Learning Techniques for Small-Footprint Keyword Spotting","authors":"Ming Sun, A. Schwarz, Minhua Wu, N. Strom, S. Matsoukas, S. Vitaladevuni","doi":"10.1109/ICMLA.2017.0-150","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-150","url":null,"abstract":"This paper presents our work on building a small-footprint keyword spotting system for a resource-limited language, which requires low CPU, memory and latency. Our keyword spotting system consists of deep neural network (DNN) and hidden Markov model (HMM), which is a hybrid DNN-HMM decoder. We investigate different transfer learning techniques to leverage knowledge and data from a resource-abundant source language to improve the keyword DNN training for a target language which has limited in-domain data. The approaches employed in this paper include training a DNN using source language data to initialize the target language DNN training, mixing data from source and target languages together in a multi-task DNN training setup, using logits computed from a DNN trained on the source language data to regularize the keyword DNN training in the target language, as well as combinations of these techniques. Given different amounts of target language training data, our experimental results show that these transfer learning techniques successfully improve keyword spotting performance for the target language, measured by the area under the curve (AUC) of DNN-HMM decoding detection error tradeoff (DET) curves using a large in-house far-field test set.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"22 1","pages":"255-260"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84316817","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: 10

Learning Long-Term Situation Prediction for Automated Driving 学习自动驾驶的长期情况预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-21

S. Hörmann, Martin Bach, K. Dietmayer

引用次数: 2