2011 10th International Conference on Machine Learning and Applications and Workshops最新文献_第9页

Using Machine Learning to Detect Cyberbullying 使用机器学习检测网络欺凌

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.152

Kelly Reynolds, April Kontostathis, Lynne Edwards

引用次数: 388

Adaptive Neuro Fuzzy Inference System, Neural Network and Support Vector Machine for Caller Behavior Classification 自适应神经模糊推理系统、神经网络和支持向量机的呼叫者行为分类

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.24

Pretesh B. Patel, T. Marwala

引用次数: 5

Street View Challenge: Identification of Commercial Entities in Street View Imagery 街景挑战:街景图像中商业实体的识别

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.181

A. Zamir, A. Darino, M. Shah

引用次数: 27

Development of a Beam Source Modeling Technique for a Flattening Filter Free (FFF) Beam 无压平滤波(FFF)光束源建模技术的发展

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.54

W. Cho, Jeong-Hoon Park, W. Jung, T. Suh, K. Kielar, E. Mok, Ruijiang Li, L. Xing

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引用次数: 0

Statistical Learning for File-Type Identification 文件类型识别的统计学习

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.135

Siddharth Gopal, Yiming Yang, Konstantin Salomatin, J. Carbonell

{"title":"Statistical Learning for File-Type Identification","authors":"Siddharth Gopal, Yiming Yang, Konstantin Salomatin, J. Carbonell","doi":"10.1109/ICMLA.2011.135","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.135","url":null,"abstract":"File-type Identification (FTI) is an important problem in digital forensics, intrusion detection, and other related fields. Using state-of-the-art classification techniques to solve FTI problems has begun to receive research attention, however, general conclusions have not been reached due to the lack of thorough evaluations for method comparison. This paper presents a systematic investigation of the problem, algorithmic solutions and an evaluation methodology. Our focus is on performance comparison of statistical classifiers (e.g. SVM and kNN) and knowledge-based approaches, especially COTS (Commercial Off-The-Shelf) solutions which currently dominate FTI applications. We analyze the robustness of different methods in handling damaged files and file segments. We propose two alternative criteria in measuring performance: 1) treating file-name extensions as the true labels, and 2) treating the predictions by knowledge based approaches on intact files, these rely on signature bytes as the true labels (and removing these signature bytes before testing each method). In our experiments with simulated damages in files, SVM and kNN substantially outperform all the COTS solutions we tested, improving classification accuracy very substantially -- some COTS methods cannot identify damaged files at all.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115210811","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: 41

State Aggregation by Growing Neural Gas for Reinforcement Learning in Continuous State Spaces 连续状态空间中用于强化学习的生长神经气体状态聚合

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.134

Michael Baumann, H. K. Büning

引用次数: 11

On a Distributed Approach for Density-Based Clustering 基于密度的分布式聚类方法研究

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.108

Nhien-An Le-Khac, Mohand Tahar Kechadi

引用次数: 4

Discriminative Optimization of String Similarity and Its Application to Biomedical Abbreviation Clustering 字符串相似度判别优化及其在生物医学缩写聚类中的应用

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.58

Atsuko Yamaguchi, Yasunori Yamamoto, Jin-Dong Kim, T. Takagi, A. Yonezawa

引用次数: 0

Adaptive Profit Sharing Reinforcement Learning Method for Dynamic Environment 动态环境下的自适应利润分享强化学习方法

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.25

Sadamori Koujaku, Kota Watanabe, H. Igarashi

引用次数: 1

Classification of Patients Using Novel Multivariate Time Series Representations of Physiological Data 使用新的多变量时间序列表示生理数据的患者分类

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.46

Patricia Ordóñez, T. Armstrong, T. Oates, J. Fackler

引用次数: 9