2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

A Review of Machine Learning Solutions to Denial-of-Services Attacks in Wireless Sensor Networks 无线传感器网络中拒绝服务攻击的机器学习解决方案综述

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.202

S. Gunduz, Bilgehan Arslan, Mehmet C. Demirci

引用次数: 20

Scrubbing the Web for Association Rules: An Application in Predictive Text 为关联规则擦洗Web:在预测文本中的应用

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.54

Justin Lovinger, I. Valova

引用次数: 3

Performance Investigation of UCB Policy in Q-learning q -学习中UCB策略的绩效调查

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.59

Koki Saito, A. Notsu, S. Ubukata, Katsuhiro Honda

引用次数: 3

Artificial Neural Network Based Abdominal Organ Segmentations: A Review 基于人工神经网络的腹部器官分割研究进展

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.231

Evgin Göçeri, Esther Martinez

引用次数: 6

Speaker Identification in Medical Simulation Data Using Fisher Vector Representation 基于Fisher向量表示的医学模拟数据说话人识别

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.187

Shuangshuang Jiang, H. Frigui, A. Calhoun

引用次数: 2

RPC: An Efficient Classifier Ensemble Using Random Projections RPC:使用随机投影的高效分类器集成

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.193

Lovedeep Gondara

引用次数: 4

Car Following Markov Regime Classification and Calibration 汽车跟随马尔可夫状态分类与标定

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.126

A. B. Zaky, W. Gomaa, Mohamed A. Khamis

{"title":"Car Following Markov Regime Classification and Calibration","authors":"A. B. Zaky, W. Gomaa, Mohamed A. Khamis","doi":"10.1109/ICMLA.2015.126","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.126","url":null,"abstract":"The car following behavior has recently gained much attention due to its wide variety of applications. This includes accident analysis, driver assessment, support systems, and road design. In this paper, we present a model that leverages Markov regime switching models to classify various car following regimes. The detected car following regimes are then mined to calibrate the parameters of drivers to be dependent on the driver's current driving regime. A two stage Markov regime switching model is utilized to detect different car following regimes. The first stage discriminates normal car following regimes from abnormal ones, while the second stage classifies normal car following regimes to their fine-grained regimes like braking, accelerating, standing, free-flowing, and normal following. A genetic algorithm is then employed to the observed driver data in each car following regime to optimize car following model parameter values of the driver in each regime. Experimental evaluation of the proposed model using a real dataset shows that it can detect up-normal (rare and short time) events. In addition, it can infer the switching process dynamics such as the expected duration, the probability of moving from one regime to another and the switching parameters of each regime. Finally, the model is able to accurately calibrate the parameters of drivers according to their driving regimes, so we can achieve a better understanding of drivers behavior and better simulation of driving situation.","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":"134460181","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: 9

Class Decomposition Using K-Means and Hierarchical Clustering 基于k均值和层次聚类的类分解

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.169

Shadi Banitaan, A. B. Nassif, Mohammad Azzeh

引用次数: 8

Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic Forgetting 序列协方差矩阵估计及其在减轻灾难性遗忘中的应用

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.109

Tomer Lancewicki, Benjamin Goodrich, I. Arel

{"title":"Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic Forgetting","authors":"Tomer Lancewicki, Benjamin Goodrich, I. Arel","doi":"10.1109/ICMLA.2015.109","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.109","url":null,"abstract":"Catastrophic forgetting is a problem encountered with neural networks as well as other learning systems whereby past representations are lost as new representations are learned. It has been shown that catastrophic forgetting can be mitigated in neural networks by using a neuron selection technique, dubbed \"cluster-select,\" which performs online clustering over the network inputs to partition the network such that only a subset of neurons are used for a given input vector. Cluster-select can benefit by using Mahalanobis distance which relies on an inverse covariance estimate. Unfortunately, covariance estimation is problematic when lacking a very large number of samples relative to the number of input dimensions. One way to tackle this problem is through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix with the aim of minimizing the mean-squared error (MSE). In online environments, such as those in which catastrophic forgetting can occur, data arrives sequentially, requiring the covariance matrix to be estimated sequentially. Therefore, in this work we derive sequential update rules for the shrinkage estimator and approximate it's related inverse. The online covariance estimator is applied to the cluster-select technique with results that demonstrate further improvements in terms of effectively mitigating catastrophic forgetting.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"32 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":"132789325","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

A Highly Distributable Computational Framework for Fast Cloud Data Retrieval 一种用于快速云数据检索的高度分布式计算框架

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.96

Amir H. Basirat, Asad I. Khan, B. Srinivasan

{"title":"A Highly Distributable Computational Framework for Fast Cloud Data Retrieval","authors":"Amir H. Basirat, Asad I. Khan, B. Srinivasan","doi":"10.1109/ICMLA.2015.96","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.96","url":null,"abstract":"Unlike the existing relational, hierarchical and object-oriented schemes, associative models can analyze data in similar ways to which our brain links information. Such interactions when implemented in voluminous data clouds can assist in searching for overarching relations in complex and highly distributed data sets with speed and accuracy. In this paper, a different perspective of data recognition will be considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this paper will be focusing on distributed processing approach for scalable data recognition and processing through applying an access scheme that will enable fast data retrieval across multiple records and data segments associatively, utilizing a parallel approach. Doing so will yield a new form of databaselike functionality that can scale up or down over the available infrastructure without interruption or degradation, dynamically and automatically. In our proposed model, data records are treated as patterns. As a result, data storage and retrieval is performed using a distributed pattern recognition approach that is implemented through the integration of loosely-coupled computational networks, followed by a divide-and-distribute approach that facilitates distribution of these networks within the cloud dynamically.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"26 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":"133155826","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