2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第4页

Online Orthogonal Regression Based on a Regularized Squared Loss 基于正则化平方损失的在线正交回归

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00150

Roberto C. S. N. P. Souza, S. C. Leite, Wagner Meira, Jr, Eduardo R. Hruschka

{"title":"Online Orthogonal Regression Based on a Regularized Squared Loss","authors":"Roberto C. S. N. P. Souza, S. C. Leite, Wagner Meira, Jr, Eduardo R. Hruschka","doi":"10.1109/ICMLA.2018.00150","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00150","url":null,"abstract":"Orthogonal regression extends the classical regression framework by assuming that the data may contain errors in both the dependent and independent variables. Often, this approach tends to outperform classical regression in real-world scenarios. However, the algorithms used to determine a solution to the orthogonal regression problem require the computation of singular value decompositions (SVD), which may be computationally expensive and impractical for real-world problems. In this work, we propose a new approach to the orthogonal regression problem based on a regularized squared loss. The method follows an online learning strategy which makes it more flexible for different types of applications. The algorithm is derived in primal and dual variables and the later formulation allows the introduction of kernels for nonlinear modeling. We compare our proposed orthogonal regression algorithm to a corresponding classical regression algorithm using both synthetic and real-world datasets from different applications. Our algorithm achieved better results for most of the datasets.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"925-930"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81156558","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

Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders 基于去噪自编码器的事件流半监督分类

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00027

Sebastian Kauschke, M. Mühlhäuser, Johannes Fürnkranz

引用次数: 1

Worker Filtering with Limited Supervision in Crowdsourcing Systems 众包系统中有限监督下的工人过滤

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00128

Lingyu Lyu, M. Kantardzic, Hanqing Hu

引用次数: 1

STARLORD: Sliding Window Temporal Accumulate-Retract Learning for Online Reasoning on Datastreams 数据流在线推理的滑动窗口时间累积-收缩学习

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00181

Cristian Axenie, R. Tudoran, S. Bortoli, Mohamad Al Hajj Hassan, D. Foroni, G. Brasche

{"title":"STARLORD: Sliding Window Temporal Accumulate-Retract Learning for Online Reasoning on Datastreams","authors":"Cristian Axenie, R. Tudoran, S. Bortoli, Mohamad Al Hajj Hassan, D. Foroni, G. Brasche","doi":"10.1109/ICMLA.2018.00181","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00181","url":null,"abstract":"Nowadays, data sources, such as IoT devices, financial markets, and online services, continuously generate large amounts of data. Such data is usually generated at high frequencies and is typically described by non-stationary distributions. Querying these data sources brings new challenges for machine learning algorithms, which now need to be considered from the perspective of an evolving stream and not a static dataset. Under such scenarios, where data flows continuously, the challenge is how to transform the vast amount of data into information and knowledge, and how to adapt to data changes (i.e. drifts) and accumulate experience over time to support online decision-making. In this paper, we introduce STARLORD, a novel incremental computation method and system acting on data streams and capable of achieving low-latency (millisecond level) and high-throughput (thousands events/second/core) when learning from data streams. Moreover, the approach is able to adapt to data drifts and accumulate experience over time, and to use such knowledge to improve future learning and prediction performance, with resource usage guarantees. This is proven by our preliminary experiments where we built-in the framework in an open source stream engine (i.e. Apache Flink).","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"79 9 1","pages":"1115-1122"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89554388","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

Machine Cognition of Violence in Videos Using Novel Outlier-Resistant VLAD 基于新型抗离群值VLAD的视频暴力机器认知

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00161

Tonmoay Deb, Aziz Arman, A. Firoze

{"title":"Machine Cognition of Violence in Videos Using Novel Outlier-Resistant VLAD","authors":"Tonmoay Deb, Aziz Arman, A. Firoze","doi":"10.1109/ICMLA.2018.00161","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00161","url":null,"abstract":"Understanding highly accurate and real-time violent actions from surveillance videos is a demanding challenge. Our primary contribution of this work is divided into two parts. Firstly, we propose a computationally efficient Bag-of-Words (BoW) pipeline along with improved accuracy of violent videos classification. The novel pipeline's feature extraction stage is implemented with densely sampled Histogram of Oriented Gradients (HOG) and Histogram of Optical Flow (HOF) descriptors rather than Space-Time Interest Point (STIP) based extraction. Secondly, in encoding stage, we propose Outlier-Resistant VLAD (OR-VLAD), a novel higher order statistics-based feature encoding, to improve the original VLAD performance. In classification, efficient Linear Support Vector Machine (LSVM) is employed. The performance of the proposed pipeline is evaluated with three popular violent action datasets. On comparison, our pipeline achieved near perfect classification accuracies over three standard video datasets, outperforming most state-of-the-art approaches and having very low number of vocabulary size compared to previous BoW Models.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"24 1","pages":"989-994"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89955656","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: 12

Supervised Max Hashing for Similarity Image Retrieval 监督最大哈希相似图像检索

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00060

Al Kobaisi, P. Wocjan

{"title":"Supervised Max Hashing for Similarity Image Retrieval","authors":"Al Kobaisi, P. Wocjan","doi":"10.1109/ICMLA.2018.00060","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00060","url":null,"abstract":"The storage efficiency of hash codes and their application in the fast approximate nearest neighbor search, along with the explosion in the size of available labeled image datasets caused an intensive interest in developing learning based hash algorithms recently. In this paper, we present a learning based hash algorithm that utilize ordinal information of feature vectors. We have proposed a novel mathematically differentiable approximation of $argmax$ function for this hash algorithm. It has enabled seamless integration of hash function with deep neural network architecture which can exploit the rich feature vectors generated by convolutional neural networks. We have also proposed a loss function for the case that the hash code is not binary and its entries are digits of arbitrary k-ary base. The resultant model comprised of feature vector generation and hashing layer is amenable to end-to-end training using gradient descent methods. In contrast to the majority of current hashing algorithms that are either not learning based or use hand-crafted feature vectors as input, simultaneous training of the components of our system results in better optimization. Extensive evaluations on NUS-WIDE, CIFAR-10 and MIRFlickr benchmarks show that the proposed algorithm outperforms state-of-art and classical data agnostic, unsupervised and supervised hashing methods by 2.6% to 19.8% mean average precision under various settings.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"226 1","pages":"359-365"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86686976","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

Multilinear Discriminant Analysis Through Tensor-Tensor Eigendecomposition 基于张量-张量特征分解的多线性判别分析

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00093

Kyle A. Caudle, R. Hoover, Karen S. Braman

引用次数: 3

Neural Machine Translation Advised by Statistical Machine Translation: The Case of Farsi-Spanish Bilingually Low-Resource Scenario 基于统计机器翻译的神经机器翻译:以波斯语-西班牙语双语低资源场景为例

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00196

Benyamin Ahmadnia, Parisa Kordjamshidi, Gholamreza Haffari

引用次数: 11

A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data 模拟半导体晶圆测试数据过程模式识别的监督方法比较

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00131

Stefan Schrunner, Olivia Bluder, Anja Zernig, Andre Kästner, Roman Kern

{"title":"A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data","authors":"Stefan Schrunner, Olivia Bluder, Anja Zernig, Andre Kästner, Roman Kern","doi":"10.1109/ICMLA.2018.00131","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00131","url":null,"abstract":"The semiconductor industry is currently leveraging to exploit machine learning techniques to improve and automate the manufacturing process. An essential step is the wafer test, where each single device is measured electrically, resulting in an image of the wafer. Our work is based on the hypothesis that deviations of production processes can be detected via spatial patterns on these wafermaps. Supervised learning methods are one possibility to recognize such patterns in an automated way - however, the training sample size is very low. In our work, we present and compare several methods for multiclass classification, which can deal with this limitation: multiclass decision trees, as well as decomposition methods like round robin and error-correcting output coding (ECOC). As elementary classifiers, we compare binary decision trees and logistic regression using an elastic net regularization. The evaluation shows that the decomposition methods outperform the multiclass decision tree regarding both, accuracy and practical demands.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"820-823"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89016956","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: 7

Classification of Eye Tracking Data Using a Convolutional Neural Network 基于卷积神经网络的眼动追踪数据分类

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00085

Yuehan Yin, Chung-jen Juan, J. Chakraborty, M. P. McGuire

{"title":"Classification of Eye Tracking Data Using a Convolutional Neural Network","authors":"Yuehan Yin, Chung-jen Juan, J. Chakraborty, M. P. McGuire","doi":"10.1109/ICMLA.2018.00085","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00085","url":null,"abstract":"Historically, eye tracking analysis has been a useful approach to identify areas of interest (AOIs) where users have specific regions of the user interface (UI) in which they are interested. Many algorithms have been proposed to analyze eye tracking data in order to make user interfaces more effective. The objective of this study is to use convolutional neural networks (CNNs) to classify eye tracking data. First, a CNN was used to classify two different web interfaces for browsing news data. Then in a second experiment, a CNN was used to classify the nationalities of users. In addition, techniques of data-preprocessing and feature-engineering were applied. The algorithm used in this research is convolutional neural network (CNN), which is famous in deep learning field. Keras framework running on top of TensorFlow was used to define and train our CNN model. The purpose of this research is to explore how feature-engineering can affect evaluation metrics about our model. The results of the study show a number of interesting patterns and generally that deep learning shows promise in the analysis of eye tracking data.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"128 1","pages":"530-535"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78691999","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: 12