2012 IEEE International Workshop on Machine Learning for Signal Processing最新文献_第8页

On the performance of histogram-based entropy estimators 基于直方图熵估计器的性能研究

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349727

C. Giurcăneanu, Panu Luosto, P. Kontkanen

引用次数: 1

Sequential anomaly detection in a batch with growing number of tests: Application to network intrusion detection 具有越来越多测试的批处理顺序异常检测:在网络入侵检测中的应用

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349793

David J. Miller, Fatih Kocak, G. Kesidis

{"title":"Sequential anomaly detection in a batch with growing number of tests: Application to network intrusion detection","authors":"David J. Miller, Fatih Kocak, G. Kesidis","doi":"10.1109/MLSP.2012.6349793","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349793","url":null,"abstract":"For high (N)-dimensional feature spaces, we consider detection of an unknown, anomalous class of samples amongst a batch of collected samples (of size T), under the null hypothesis that all samples follow the same probability law. Since the features which will best identify the anomalies are a priori unknown, several common detection strategies are: 1) evaluating atypicality of a sample (its p-value) based on the null distribution defined on the full N-dimensional feature space; 2) considering a (combinatoric) set of low order distributions, e.g. all singletons and all feature pairs, with detections made based on the smallest p-value yielded over all such low order tests. The first approach relies on accurate estimation of the joint distribution, while the second may suffer from increased false alarm rates as N and T grow. Alternatively, inspired by greedy feature selection commonly used in supervised learning, we propose a novel sequential anomaly detection procedure with a growing number of tests. Here, new tests are (greedily) included only when they are needed, i.e., when their use (on currently undetected samples) will yield greater aggregate statistical significance of (multiple testing corrected) detections than obtainable using the existing test cadre. Our approach thus aims to maximize aggregate statistical significance of all detections made up until a finite horizon. Our method is evaluated, along with supervised methods, for a network intrusion domain, detecting Zeus bot (intrusion) packet flows embedded amongst (normal)Web flows. It is shown that judicious feature representation is essential for discriminating Zeus from Web.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128018012","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

On comparing hard and soft fusion of dependent detectors 依赖探测器硬、软聚变的比较

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349792

A. Soriano, L. Vergara, G. Safont, A. Salazar

引用次数: 4

Temporal context in object recognition 对象识别中的时间背景

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349758

R. Chalasani, J. Príncipe

引用次数: 2

GLRT for testing separability of a complex-valued mixture based on the Strong Uncorrelating Transform 基于强不相关变换的复值混合物可分性检测

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349785

D. Ramírez, P. Schreier, J. Vía, I. Santamaría

引用次数: 2

ESWT - tracking organs during focused ultrasound surgery 聚焦超声手术中ESWT跟踪器官

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349746

C. Grozea, D. Lübke, Felix Dingeldey, M. Schiewe, J. Gerhardt, C. Schumann, J. Hirsch

{"title":"ESWT - tracking organs during focused ultrasound surgery","authors":"C. Grozea, D. Lübke, Felix Dingeldey, M. Schiewe, J. Gerhardt, C. Schumann, J. Hirsch","doi":"10.1109/MLSP.2012.6349746","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349746","url":null,"abstract":"We report here our results in a multi-sensor setup reproducing the conditions of an automated focused ultrasound surgery environment. The aim is to continuously predict the position of an internal organ (here the liver) under guided and non-guided free breathing, with the accuracy required by surgery. We have performed experiments with 16 healthy human subjects, two of those taking part in full-scale experiments involving a 3 Tesla MRI machine recording a volume containing the liver. For the other 14 subjects we have used the optical tracker as a surrogate target. All subjects where volunteers who agreed to participate in the experiments after being thoroughly informed about it. For the MRI sessions we have analyzed semi-automatically offline the images in order to obtain the ground truth, the true position of the selected feature of the liver. The results we have obtained with continuously updated random forest models are very promising, we have obtained good prediction-target correlation coefficients for the surrogate targets (0.71 ± 0.1) and excellent for the real targets in the MRI experiments (over 0.91), despite being limited to a lower model update frequency, once every 6.16 seconds.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508093","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

Nearest neighbor-based importance weighting 基于最近邻的重要性加权

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349714

M. Loog

引用次数: 31

The eighth annual MLSP competition: First place team 第八届MLSP年度大赛:第一名队伍

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349771

Ankit Gupta, Shashwati Mishra, A. Mukerjee

引用次数: 1

Semisupervised kernel orthonormalized partial least squares 半监督核正则化偏最小二乘

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349718

E. Izquierdo-Verdiguier, J. Arenas-García, Sergio Muñoz-Romero, L. Gómez-Chova, Gustau Camps-Valls

引用次数: 4

Learning smooth models of nonsmooth functions via convex optimization 通过凸优化学习非光滑函数的光滑模型

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349755

Fabien Lauer, Van Luong Le, G. Bloch

引用次数: 13