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2008 Seventh International Conference on Machine Learning and Applications最新文献

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Chi-Sim: A New Similarity Measure for the Co-clustering Task Chi-Sim:一种新的共聚类相似性度量
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.103
G. Bisson, Syed Fawad Hussain
{"title":"Chi-Sim: A New Similarity Measure for the Co-clustering Task","authors":"G. Bisson, Syed Fawad Hussain","doi":"10.1109/ICMLA.2008.103","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.103","url":null,"abstract":"Co-clustering has been widely studied in recent years. Exploiting the duality between objects and features efficiently helps in better clustering both objects and features. In contrast with current co-clustering algorithms that focus on directly finding some patterns in the data matrix, in this paper we define a (co-)similarity measure, named X-Sim, which iteratively computes the similarity between objects and their features. Thus, it becomes possible to use any clustering methods (k-means, ...) to co-cluster data. The experiments show that our algorithm not only outperforms the classical similarity measure but also outperforms some co-clustering algorithms on the document-clustering task.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115498113","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: 62
Active Stereo Olfactory Sensing System for Localization of Gas/Odor Source 用于气体/气味源定位的主动立体嗅觉传感系统
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.101
Atsushi Kohnotoh, H. Ishida
{"title":"Active Stereo Olfactory Sensing System for Localization of Gas/Odor Source","authors":"Atsushi Kohnotoh, H. Ishida","doi":"10.1109/ICMLA.2008.101","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.101","url":null,"abstract":"An active stereo nose (ASNose) is proposed for localization of gas sources. Dogs actively deform their nostrils when they sniff. The air in front of a dog is drawn to the nose by inhalation, and then, ejected backward. This backward jet further induces air currents flowing from the front of the dog to the nose, which assist in collecting odors. To mimic this mechanism, ASNose is equipped with two diaphragm pumps and two gas sensors. Air is sucked through two pipes, and delivered to the two gas sensors. The exhaust of the pumps is expired backward. Moreover, backflows inevitably generated by the diaphragm pumps make air currents flowing in the direction away from ASNose and work like an air curtain. Experimental results show that the actively generated air currents facilitate the directional determination of a gas source by enhancing the difference between the right and left gas sensor responses.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127506535","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: 13
A Multi-expert Classification Framework with Transferable Voting for Intrusion Detection 基于可转移投票的入侵检测多专家分类框架
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.18
Tich Phuoc Tran, Pohsiang Tsai, T. Jan
{"title":"A Multi-expert Classification Framework with Transferable Voting for Intrusion Detection","authors":"Tich Phuoc Tran, Pohsiang Tsai, T. Jan","doi":"10.1109/ICMLA.2008.18","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.18","url":null,"abstract":"Network security is a critical component for any sized organization. While static defence technologies such as firewalls and anti-virus provide basic protection for computer networks, an intrusion detection system (IDS) can improve overall security by identifying and responding to novel malicious activities. The current existing IDS methods suffer from low accuracy and system robustness. To overcome such limitations, this paper proposes a multi-expert classification framework for detecting different types of network anomalies. Specifically, different types of intrusions will be detected with different strategies, including different attribute selections and learning algorithms. Several voting approaches are also investigated for the purpose of classifier combination. The Knowledge Discovery and Data Mining (KDD-99) dataset is used as a benchmark to compare this method with other existing techniques. Empirical results indicate that the proposed design outperforms other state-of-the-art learning methods in terms of detection cap abilities, misclassification cost and processing overheads.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114161587","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
Temporal Exemplar-Based Bayesian Networks for Facial Expression Recognition 基于时间样本的贝叶斯网络面部表情识别
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.9
Lifeng Shang, Kwok-Ping Chan
{"title":"Temporal Exemplar-Based Bayesian Networks for Facial Expression Recognition","authors":"Lifeng Shang, Kwok-Ping Chan","doi":"10.1109/ICMLA.2008.9","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.9","url":null,"abstract":"We present a Temporal Exemplar-based Bayesian Networks (TEBNs) for facial expression recognition. The proposed Bayesian Networks (BNs) consists of three layers: Observation layer, Exemplars layer and Prior Knowledge layer. In the Exemplars layer, exemplar-based model is integrated with BNs to improve the accuracy of probability estimation. In the Prior Knowledge layer, static BNs is extended to Temporal BNs by considering historical observations to model temporal behavior of facial expression. Experiment on CMU expression database illustrates that the proposed TEBNs is very efficient in modeling the evolution of facial deformation.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115353202","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: 8
A Comparative Study of Selected Classification Accuracy in User Profiling 用户画像中选择分类准确率的比较研究
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.139
A. Cufoglu, M. Lohi, K. Madani
{"title":"A Comparative Study of Selected Classification Accuracy in User Profiling","authors":"A. Cufoglu, M. Lohi, K. Madani","doi":"10.1109/ICMLA.2008.139","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.139","url":null,"abstract":"In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377433","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: 8
Toward Chemical-Trail Following Robots 化学轨迹跟踪机器人
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.133
P. Sousa, Lino Marques, A. T. Almeida
{"title":"Toward Chemical-Trail Following Robots","authors":"P. Sousa, Lino Marques, A. T. Almeida","doi":"10.1109/ICMLA.2008.133","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.133","url":null,"abstract":"This paper presents new advances in the area of chemical trail following with mobile robots. For that purpose, the properties of chemical volatiles and their evaporation rates were tested in order to discover an appropriate candidate to mark chemical trails and at the same time with ability to be detected by the robot olfactory system along the time. The capability of chemical detection was provide by a metal oxide-based olfactory system, mounted in a Khepera III mobile robot. An algorithm to search and track chemical trails is proposed. This algorithm was validated by simulations and by laboratory tests with a Khepera III mobile robot.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123558099","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
Knowledge-Supervised Learning by Co-clustering Based Approach 基于协聚类的知识监督学习方法
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.116
Congle Zhang, Dikan Xing
{"title":"Knowledge-Supervised Learning by Co-clustering Based Approach","authors":"Congle Zhang, Dikan Xing","doi":"10.1109/ICMLA.2008.116","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.116","url":null,"abstract":"Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description usually does not contain enough information to guide classification. Fortunately, large amount of public data is easily acquired, i.e. ODP, Wikipedia and so on, which contains enormous knowledge. In this paper, we address the text classification problem with such knowledge rather than any labeled documents and propose a co-clustering based knowledge-supervised learning algorithm (CoCKSL) in information theoretic framework, which effectively applies the knowledge to classification tasks.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128895213","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
Missing Data Imputation in Longitudinal Cohort Studies: Application of PLANN-ARD in Breast Cancer Survival 纵向队列研究中的缺失数据输入:PLANN-ARD在乳腺癌生存中的应用
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.106
A. S. Fernandes, I. Jarman, T. Etchells, J. M. Fonseca, E. Biganzoli, C. Bajdik, P. Lisboa
{"title":"Missing Data Imputation in Longitudinal Cohort Studies: Application of PLANN-ARD in Breast Cancer Survival","authors":"A. S. Fernandes, I. Jarman, T. Etchells, J. M. Fonseca, E. Biganzoli, C. Bajdik, P. Lisboa","doi":"10.1109/ICMLA.2008.106","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.106","url":null,"abstract":"Missing values are common in medical datasets and may be amenable to data imputation when modelling a given data set or validating on an external cohort. This paper discusses model averaging over samples of the imputed distribution and extends this approach to generic non-linear modelling with the Partial Logistic Artificial Neural Network (PLANN) regularised within the evidence-based framework with Automatic Relevance Determination (ARD). The study then applies the imputation to external validation over new patient cohorts, considering also the case of predictions made for individual patients. A prognostic index is defined for the non-linear model and validation results show that 4 statistically significant risk groups identified at the 95% level of confidence from the modelling data, from Christie Hospital (n=931), retain good separation during external validation with data from the British Columbia Cancer Agency (n=4,083).","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359453","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: 8
Basis Function Construction in Reinforcement Learning Using Cascade-Correlation Learning Architecture 基于级联-相关学习架构的强化学习中的基函数构建
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.24
Sertan Girgin, P. Preux
{"title":"Basis Function Construction in Reinforcement Learning Using Cascade-Correlation Learning Architecture","authors":"Sertan Girgin, P. Preux","doi":"10.1109/ICMLA.2008.24","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.24","url":null,"abstract":"In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data in a more informative form that facilitates and improves subsequent steps. As a \"good'' set of basis functions result in better solutions and defining such functions becomes a challenge with increasing problem complexity, it is beneficial to be able to generate them automatically. In this paper, we propose a new approach based on Bellman residual for constructing basis functions using cascade-correlation learning architecture. We show how this approach can be applied to Least Squares Policy Iteration algorithm in order to obtain a better approximation of the value function, and consequently improve the performance of the resulting policies. We also present the effectiveness of the method empirically on some benchmark problems.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130246023","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
Graph-Based Multilevel Dimensionality Reduction with Applications to Eigenfaces and Latent Semantic Indexing 基于图的多层次降维及其在特征面和潜在语义索引中的应用
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.140
Sophia Sakellaridi, Haw-ren Fang, Y. Saad
{"title":"Graph-Based Multilevel Dimensionality Reduction with Applications to Eigenfaces and Latent Semantic Indexing","authors":"Sophia Sakellaridi, Haw-ren Fang, Y. Saad","doi":"10.1109/ICMLA.2008.140","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.140","url":null,"abstract":"Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can be time-consuming when the data set is large. This paper presents a multilevel framework to reduce the size of the data set, prior to performing dimension reduction. The algorithm exploits nearest-neighbor graphs. It recursively coarsens the data by finding a maximal matching level by level. The coarsened data at the lowest level is then projected using a known linear dimensionality reduction method. The same linear mapping is performed on the original data set, and on any new test data. The methods are illustrated on two applications: eigenfaces (face recognition) and latent semantic indexing (text mining). Experimental results indicate that the multilevel techniques proposed here offer a very appealing cost to quality ratio.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513482","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: 17
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