2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第10页

Evaluating the Uncertainty of a Bayesian Network Query Response by Using Joint Probability Distribution 用联合概率分布评价贝叶斯网络查询响应的不确定性

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.115

Yang Shao, Toshinori Miyoshi, Yasutaka Hasegawa, Hideyuki Ban

{"title":"Evaluating the Uncertainty of a Bayesian Network Query Response by Using Joint Probability Distribution","authors":"Yang Shao, Toshinori Miyoshi, Yasutaka Hasegawa, Hideyuki Ban","doi":"10.1109/ICMLA.2015.115","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.115","url":null,"abstract":"Bayesian network is a powerful tool to represent patterns inside past data. It can be used to predict future by calculating the posterior probability of future events. Machine learning techniques that can construct a Bayesian network from past data automatically are well developed in recent years. If we consider past data as a sampling set from an original probabilistic distribution, the \"learning\" process is actually trying to reproduce the original probabilistic distribution from the sampling set. Therefore, the finiteness of size of sampling set will bring uncertainties to the reproduced parameters of constructed Bayesian network. When the constructed Bayesian network is used to predict future, the uncertainties of reproduced parameters will be transferred to the uncertainty of query response. Here, the query response is the posterior probability that we are interested in. Evaluating the uncertainty of query response is critical to some strict industrial applications. Previous researches have proposed a method to evaluate the uncertainty. The consequence is shown as a variance of the query response. However, the conventional method need to work together with the bucket elimination, an exact inference method. Therefore, the conventional method can not deal with large Bayesian networks that used in real applications because of its calculation cost. We proposed a new approach to calculate the uncertainty of query responses by using joint probability distribution in this research. The proposed method can work with any inference method. Therefore, it can give an approximate evaluation even when the Bayesian network is large by using an approximate inference method. To investigate the accuracy of our proposed method, six well used public Bayesian networks are used as test cases. By comparing the approximate results with the exact results, an average error of -13.60% is got.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130317974","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

Boosting the Detection of Malicious Documents Using Designated Active Learning Methods 使用指定的主动学习方法增强恶意文档的检测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.52

N. Nissim, Aviad Cohen, Y. Elovici

{"title":"Boosting the Detection of Malicious Documents Using Designated Active Learning Methods","authors":"N. Nissim, Aviad Cohen, Y. Elovici","doi":"10.1109/ICMLA.2015.52","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.52","url":null,"abstract":"Most organizations usually create, send and receive huge amounts of documents daily, Attackers increasingly take advantage of innocent users who tend to casually open email massages assumed to be benign, carrying malicious documents. Recent targeted attacks aimed at organizations, utilize the new Microsoft Word documents (*.docx). Anti-virus software fails to detect new unknown malicious files, including malicious docx files. In this study, we present SFEM feature extraction methodology and designated Active Learning (AL) methods, aimed at accurate detection of new unknown malicious docx files that also efficiently enhances the detection's model capabilities over time. Our AL methods identify and acquire only small set of new docx files that are most likely malicious, as well as informative benign files, these files are used for enhancing the knowledge stores of both the detection model and the anti-virus software. Results show that our active learning methods used only 14% of the labeled docx files within organization which led to a reduction of 95.5% in labeling efforts compared to passive learning and SVM-Margin (existing active learning method). Our AL methods also showed a significant improvement of 91% in unknown docx malware acquisition compared to passive learning and SVM-Margin, thus providing an improved updating solution for detection model, as well as the anti-virus software widely used within organizations.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127111091","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

Decaying Potential Fields Neural Network: An Approach for Parallelizing Topologically Indicative Mapping Exemplars 衰减势场神经网络:一种并行化拓扑指示性映射范例的方法

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.56

Clint Rogers, I. Valova

{"title":"Decaying Potential Fields Neural Network: An Approach for Parallelizing Topologically Indicative Mapping Exemplars","authors":"Clint Rogers, I. Valova","doi":"10.1109/ICMLA.2015.56","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.56","url":null,"abstract":"Mapping methodologies aim to make sense or connections from hard data. The human mind is able to efficiently and quickly process images through the visual cortex, in part due to its parallel nature. A basic Kohonen self-organizing feature map (SOFM) is one example of a mapping methodology in the class of neural networks that does this very well. Optimally the result is a nicely mapped neural network representative of the data set, however SOFMs do not translate to a parallelized architecture very well. The problem stems from the neighborhoods that are established between the neurons, creating race conditions for updating winning neurons. We propose a fully parallelized mapping architecture based loosely on SOFM called decaying potential fields neural network (DPFNN). We show that DPFNN uses neurons that are computationally uncoupled but symbolically linked. Through analysis we show this allows for the neurons to reach convergence with having only a passive data dependency on each other, as opposed to a hazard generating direct dependency. We have created this network to closely reflect the efficiency and speed of a parallel approach, with results that rival or exceed those of similar topological networks such as SOFM.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129535549","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

Hidden Markov Support Vector Machines for Self-Paced Brain Computer Interfaces 自定步脑机接口的隐马尔可夫支持向量机

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.178

H. Bashashati, R. Ward, A. Bashashati

{"title":"Hidden Markov Support Vector Machines for Self-Paced Brain Computer Interfaces","authors":"H. Bashashati, R. Ward, A. Bashashati","doi":"10.1109/ICMLA.2015.178","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.178","url":null,"abstract":"Brain Computer Interfaces (BCI) aim at providing a means to control devices with brain signals. Self-paced BCIs, as opposed to synchronous ones, have the advantage of being operational at all times and not only at specific system-defined periods. Traditionally, in the BCI field, a sliding window over the brain signal is used to detect the intention of the user at a given time. This approach ignores the temporal correlations between the adjacent time windows. This paper proposes a novel approach to classify self-paced BCI data using structural support vector machines. Our proposed approach considers the history of the brain signals in the context of sequential supervised learning to better detect the intention of the user from his/her brain signals. We have compared our proposed model to the sliding window approach with Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) classifiers. Using data collected from 4 individuals form BCI competition IV, it is shown that the F1 score of our approach is significantly better than the sliding window approach. The average F1 score of our method across all subjects is 0.3 and 0.5 higher than the sliding window with SVM and LDA classifiers, respectively.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129008598","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

Prediction of Sunspot Number Using Minimum Error Entropy Cost Based Kernel Adaptive Filters 基于最小误差熵代价的核自适应滤波器预测太阳黑子数

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.173

P. Brahma

{"title":"Prediction of Sunspot Number Using Minimum Error Entropy Cost Based Kernel Adaptive Filters","authors":"P. Brahma","doi":"10.1109/ICMLA.2015.173","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.173","url":null,"abstract":"Several algorithms in adaptive filtering are based on the minimization of the mean squared error (MSE) cost function. However, MSE is just a second order statistics and hence does not capture the entire information about the probability distribution of the error in the system. An information theoretic alternative is using the minimum error entropy (MEE) cost function. Adaptive algorithms based on this criterion have been developed and shown to be superior as compared to MSE counterparts. In this work, kernel versions of some of these methods are designed and tested on predicting the annual sunspot number. The sunspot number is the number of visibly darker regions on the solar surface and has been shown to be instrumental in modeling space weather, state of the ionosphere, climatic anomalies and even global warming. A comparative performance study of the various linear and kernel algorithms, trained with both MEE and MSE criteria, in predicting such a chaotic non-linear time series is presented in this paper. Experimental results clearly show the advantage of the MEE based kernel design which is as per expectation given that it has the advantage of being non-linear along with being able to derive maximum information from the error distribution.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129204242","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

A Model of Local Binary Pattern Feature Descriptor for Valence Facial Expression Classification 一种用于价态表情分类的局部二值模式特征描述子模型

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.185

Ruth Agada, Jie Yan

引用次数: 3

Learning Convex Piecewise Linear Machine for Data-Driven Optimal Control 学习凸分段线性机的数据驱动最优控制

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.43

Yuxun Zhou, Baihong Jin, C. Spanos

{"title":"Learning Convex Piecewise Linear Machine for Data-Driven Optimal Control","authors":"Yuxun Zhou, Baihong Jin, C. Spanos","doi":"10.1109/ICMLA.2015.43","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.43","url":null,"abstract":"In a data-driven Optimal Control (OP) scheme, one or more involved components, such as objective function, system dynamics, or operation constraints, are described with statistical models and learned from data. In this work, we focus on the machine learning of operation constraints which is rarely addressed in previous research. Although a rich collection of supervised learning methods exist in literature, most of them are not suitable for modeling operation constraints, because their decision rules usually induce undesirable non-linear couplings in system variables. In order to surpass simple linear models while at the same time maintaining compatibility with downstream control applications, we propose to describe system operation requirement by convex piecewise linear machine (CPLM), which does not incur any difficulties in optimization and is directly pluggable. The generalization performance of the proposed classifier is analyzed through bounding its VC-dimension, and a large margin cost sensitive learning objective is formulated with Bayes consistent hinge loss. We solve the training problem by online stochastic gradient descent and propose a mixed integer based initialization method. A case study on Heating, Ventilation and Air Conditioning (HVAC) systems control with comfort requirement is conducted and the results show that CPLM is not only a promising candidate for cost sensitive learning in general, but also enables much better description and exploitation of the system operation region for optimal control purpose.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122434933","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: 11

Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data 利用集成、数据采样和特征选择技术提高Tweet情绪数据的分类性能

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.21

Joseph D. Prusa, T. Khoshgoftaar, Amri Napolitano

{"title":"Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data","authors":"Joseph D. Prusa, T. Khoshgoftaar, Amri Napolitano","doi":"10.1109/ICMLA.2015.21","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.21","url":null,"abstract":"Sentiment analysis of tweets is a popular method of opinion mining social media. Many machine learning techniques exist that can improve the performance of classifiers trained to determine the sentiment or emotional polarity of a tweet, however, they are designed with different objectives and it is unclear which techniques are most beneficial. Additionally, these techniques may behave differently depending on quality of data issues, such as class imbalance, a common problem when using real world data. In an effort to determine which techniques are more important, we tested 12 techniques consisting of: eight feature selection techniques, bagging, boosting and data sampling with two post sampling class ratios. Using five base learners, we compare these techniques against each other and each base learners with no additional technique. We train and test each classifier on a balanced dataset and two imbalanced datasets with different class ratios. Additionally, we conduct statistical tests to determine if the differences observed between techniques are significant. Our results show that bagging and seven of the eight feature selection techniques significantly improve performance (compared to using no technique) on all three datasets, while boosting and data sampling are less beneficial for imbalanced tweet sentiment data. To the best of our knowledge, this is the first study comparing these three types of techniques on tweet sentiment data and the first to show that feature selection and ensemble techniques perform better than data sampling on tweet sentiment data.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122816448","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

A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks 基于有限混合模型的判别学习框架

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.77

Faisal R. Al-Osaimi, N. Bouguila

{"title":"A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks","authors":"Faisal R. Al-Osaimi, N. Bouguila","doi":"10.1109/ICMLA.2015.77","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.77","url":null,"abstract":"It is well-known that classification tasks can be approached using either generative models or discriminative ones. While the goal of generative approaches is to learn class-conditional densities, the main goal of discriminative techniques is to learn decision boundaries directly without taking into account class-conditional densities. In classic supervised learning, we would usually represent a given object (an image, for instance) by a vector of D real-valued features and then select a given generative or discriminative approach to perform classification. In many applications, however, the object can be represented by a set (or) bag of vectors. Recent developments in machine learning, along with powerful computational tools, have enabled researchers to develop more sophisticated models to handle such applications using the so-called hybrid generative discriminative models. The main idea is based on exploiting the advantages of both families of models. Thus, the success of such an approach depends on the choice of an appropriate discriminative technique and a suitable generative one. The goal of this paper is to develop a hybrid generative discriminative framework based on support vector machine and Gamma mixture. In particular, we focus on the generation of kernels when examples (images, for instance) are structured data (i.e. described by sets of vectors) modeled by Gamma mixtures. Experimental results on real-world challenging applications, namely 3D shape class recognition, object categorization, and video event analysis, show the effectiveness of the proposed framework.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115393749","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

Semi Supervised Learning for Human Activity Recognition Using Depth Cameras 使用深度相机进行人类活动识别的半监督学习

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.170

Moustafa F. Mabrouk, Nagia M. Ghanem, M. Ismail

{"title":"Semi Supervised Learning for Human Activity Recognition Using Depth Cameras","authors":"Moustafa F. Mabrouk, Nagia M. Ghanem, M. Ismail","doi":"10.1109/ICMLA.2015.170","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.170","url":null,"abstract":"Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the 3D depth data captured by the promising RGB-D cameras, and particularly, the Microsoft Kinect which has made high resolution real-time depth cheaply available. Several features and descriptors have been proposed for depth based action recognition, and they have given high results when recognizing the actions, but one dilemma always exists, the labeled data given, which are manually set by humans. They are not enough to build the system, especially that the use of human action recognition is mainly for surveillance of people activities. In this paper, the paucity of labeled data is addressed, by the popular semi supervision machine learning technique \"co-training\", which makes full use of unlabeled samples of two different independent views. Through the experiments on two popular datasets (MSR Action 3D, and MSR DailyActivity 3D), we demonstrate that our proposed framework outperforms the state of art. It improves the accuracy up to 83% in case of MSR Action 3D, and up to 80% MSR DailyActivity 3D, using the same number of labeled samples.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132504730","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