2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)最新文献_第6页

Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules 作为一组If-Then规则的深度强化学习代理的可解释逼近

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00041

S. Nageshrao, Bruno Costa, Dimitar Filev

引用次数: 7

Generating Near and Far Analogies for Educational Applications: Progress and Challenges 在教育应用中产生远近类比:进展与挑战

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00316

M. Boger, A. Laverghetta, Nikolai Fetisov, John Licato

引用次数: 2

SVM-Based Segmentation of Home Appliance Energy Measurements 基于svm的家电能耗测量分割

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00272

Marc Wenninger, Dominik Stecher, Jochen Schmidt

引用次数: 5

Temporal Modeling of Deterioration Patterns and Clustering for Disease Prediction of ALS Patients ALS患者疾病预测恶化模式的时间模型和聚类

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00019

Dan Halbersberg, B. Lerner

引用次数: 3

Denoising Internet Delay Measurements using Weak Supervision 基于弱监督的网络时延测量去噪

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00089

A. Muthukumar, Ramakrishnan Durairajan

引用次数: 3

Gender Estimation from a Hybrid of Face, Upper and Full Body Images at Varying Body Poses 从不同身体姿势的面部，上身和全身图像的混合性别估计

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00312

O. Iloanusi, C. Mbah

引用次数: 1

Design of a Cost-Effective Deep Convolutional Neural Network–Based Scheme for Diagnosing Faults in Smart Grids 基于深度卷积神经网络的智能电网故障诊断方案设计

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00232

Hossein Hassani, Maryam Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, V. Palade

{"title":"Design of a Cost-Effective Deep Convolutional Neural Network–Based Scheme for Diagnosing Faults in Smart Grids","authors":"Hossein Hassani, Maryam Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, V. Palade","doi":"10.1109/ICMLA.2019.00232","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00232","url":null,"abstract":"There has been a growing interest in using smart grids due to their capability in delivering automated and distributed energy level to the consumption units. However, in order to guarantee the safe and reliable delivery of the high-quality power from the generation units to the consumers, smart grids need to be equipped with diagnostic systems. This paper presents an efficient data-driven scheme for diagnosing faults in smart grids. In order to reduce the computational burden and monitor the state of the system with a lower number of smart meters, a method based on the affinity propagation clustering algorithm is suggested for the placement of meters, that makes use of the graph-based representation of the system. The collected voltage data measurements from the installed meters are then decomposed by matching pursuit decomposition in order to generate informative features. Extracted features are then used to train a convolutional neural network, and the constructed deep learning model is then tested using unseen samples of normal and faulty conditions. Simulation results based on the IEEE 39–Bus System demonstrate the effectiveness of the proposed data-driven fault diagnostic system.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134103142","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: 6

Acoustic Scene Classification Using Deep Mixtures of Pre-trained Convolutional Neural Networks 基于深度混合预训练卷积神经网络的声学场景分类

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00151

Truc The Nguyen, Alexander Fuchs, F. Pernkopf

{"title":"Acoustic Scene Classification Using Deep Mixtures of Pre-trained Convolutional Neural Networks","authors":"Truc The Nguyen, Alexander Fuchs, F. Pernkopf","doi":"10.1109/ICMLA.2019.00151","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00151","url":null,"abstract":"We propose a heterogeneous system of Deep Mixture of Experts (DMoEs) models using different Convolutional Neural Networks (CNNs) for acoustic scene classification (ASC). Each DMoEs module is a mixture of different parallel CNN structures weighted by a gating network. All CNNs use the same input data. The CNN architectures play the role of experts extracting a variety of features. The experts are pre-trained, and kept fixed (frozen) for the DMoEs model. The DMoEs is post-trained by optimizing weights of the gating network, which estimates the contribution of the experts in the mixture. In order to enhance the performance, we use an ensemble of three DMoEs modules each with different pairs of inputs and individual CNN models. The input pairs are spectrogram combinations of binaural audio and mono audio as well as their pre-processed variations using harmonic-percussive source separation (HPSS) and nearest neighbor filters (NNFs). The classification result of the proposed system is 72.1% improving the baseline by around 12% (absolute) on the development data of DCASE 2018 challenge task 1A.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134398568","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

Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions 通过解释机器学习模型预测来理解早期儿童肥胖

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00235

Xueqin Pang, C. Forrest, F. Lê-Scherban, A. Masino

{"title":"Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions","authors":"Xueqin Pang, C. Forrest, F. Lê-Scherban, A. Masino","doi":"10.1109/ICMLA.2019.00235","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00235","url":null,"abstract":"Obesity, as an independent risk factor for increased morbidity and mortality throughout the lifecycle, is a major health issue in the United States. Pediatric obesity is a strong risk factor for adult obesity, as it tends to be stable and tracks into adulthood. Therefore, prevention of childhood obesity is urgently required for reduction in obesity prevalence and obesity related comorbidities. In this paper, the general pediatric obesity development pattern and the onset time period of early childhood obesity was identified via analysis of approximately 11 million pediatric clinical encounters of 860,510 unique individuals. XGBoost model was developed to predict at age 2 years if individuals would develop obesity in early childhood. The model is generalized to both males and females, and achieved an AUC of 81% (± 0.1%). Obesity associated risk factors were further analyzed via interpretation of the XGBoost model predictions. Besides known predictive factors such as weight, height, race, and ethnicity, new factors such as body temperature and respiratory rate were also identified. As body temperature and respiratory rate are related to human metabolism, novel physiologic mechanisms that cause these associations might be discovered in future research. We decomposed model recall to different age ranges when obesity incidence occurred. The model recall for individuals with obesity incidence between 24–36 months was 97.63%, while recall for obesity incidence between 72–84 months was 48.96%, suggesting obesity is less predictable further in the future. Since obesity is largely affected by evolving factors such as life style, diet, and living environment, it is possible that obesity prevention may be achieved via changes in adjustable factors.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131694589","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: 17

Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts 非定常物流环境下平面目标检测模型的精益训练数据生成

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00062

Laura Dörr, Felix Brandt, Anne Meyer, Martin Pouls

{"title":"Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts","authors":"Laura Dörr, Felix Brandt, Anne Meyer, Martin Pouls","doi":"10.1109/ICMLA.2019.00062","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00062","url":null,"abstract":"Supervised deep learning has become the state of the art method for object detection and is used in many application areas such as autonomous driving, manufacturing industries or security systems. The acquisition of annotated data sets for the training of neural networks is highly time-consuming and error-prone. Thus, the supervised training of such object detection models is not feasible in some cases. This holds for the task of logistics transport label detection, as this use-case stands out by requiring highly specialized, quickly adapting models whilst allowing for little manual efforts in the data preparation and training process. We propose an easy training data generation method enabling the fully automated training of specialized models for the task of logistics transport label detection. For data synthesis, we stitch instances of the transport labels to be detected into background images whilst using image degradation and augmentation methods. We evaluate the employment of both use-case-specific, carefully selected background images and randomly selected real-world background images. Further, we compare two different data generation approaches: one generating realistically looking images and a simpler one making do without any manual image annotation. We examine and evaluate the introduced method on a new and publicly available example data set relevant for logistics transport label detection. We show that accurate models can be trained exclusively on synthetic training data and we compare their performance to models trained on real, manually annotated images.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133253828","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