2011 10th International Conference on Machine Learning and Applications and Workshops最新文献_第8页

Implementation of Parameter Space Search for Meta Learning in a Data-Mining Multi-agent System 数据挖掘多智能体系统中元学习参数空间搜索的实现

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.161

O. Kazík, K. Pesková, M. Pilát, Roman Neruda

引用次数: 8

Pattern Learning through Distant Supervision for Extraction of Protein-Residue Associations in the Biomedical Literature 生物医学文献中蛋白质残基关联提取的远程监督模式学习

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.112

K. Ravikumar, Haibin Liu, J. Cohn, M. Wall, Karin M. Verspoor

引用次数: 7

Structured Multivariate Pattern Classification to Detect MRI Markers for an Early Diagnosis of Alzheimer's Disease 结构化多变量模式分类检测早期诊断阿尔茨海默病的MRI标志物

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.185

C. Damon, E. Duchesnay, M. Depecker

{"title":"Structured Multivariate Pattern Classification to Detect MRI Markers for an Early Diagnosis of Alzheimer's Disease","authors":"C. Damon, E. Duchesnay, M. Depecker","doi":"10.1109/ICMLA.2011.185","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.185","url":null,"abstract":"Multiple kernel learning (MKL) provides flexibility by considering multiple data views and by searching for the best data representation through a combination of kernels. Clinical applications of neuroimaging have seen recent upsurge of the use of multivariate machine learning methods to predict clinical status. However, they usually do not model structured information, such as cerebral spatial and functional networking, which could improve the predictive capacity of the model and which could be more meaningful for further neuroscientific interpretation. In this study, we applied a MKL-based approach to predict prodromal stage of Alzheimer disease (i.e. early phase of the illness) with prior structured knowledges about the brain spatial neighborhood structure and the brain functional circuits linked to cognitve decline of AD. Compared to a set of classical multivariate linear classifiers, each one highlighting specific strategies, the smooth MKL-SVM method (i.e. Lp MKL-SVM) appeared to be the most powerful to distinguish both very mild and mild AD patients from healthy subjets.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"28 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":"127711693","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

Web Ad-Slot Offline Scheduling Using an Ant Colony Algorithm 基于蚁群算法的Web广告时段离线调度

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.158

V. Palade, S. Banerjee

引用次数: 4

Discovering Clusters with Arbitrary Shapes and Densities in Data Streams 在数据流中发现具有任意形状和密度的簇

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.56

A. Magdy, N. A. Yousri, Nagwa M. El-Makky

{"title":"Discovering Clusters with Arbitrary Shapes and Densities in Data Streams","authors":"A. Magdy, N. A. Yousri, Nagwa M. El-Makky","doi":"10.1109/ICMLA.2011.56","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.56","url":null,"abstract":"The availability of streaming data in different fields and in various forms increases the importance of streaming data analysis. The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this paper, a new grid-based and density-based algorithm is proposed for clustering streaming data. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an online component to map the input data to grid cells. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering quality and scalability. In addition, the output quality of the proposed algorithm is less sensitive to parameter selection errors.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"27 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":"116325741","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

Metric Learning for Music Symbol Recognition 音乐符号识别的韵律学习

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.94

Ana Rebelo, J. Tkaczuk, R. Sousa, Jaime S. Cardoso

引用次数: 15

Infinite Dirichlet Mixture Model and Its Application via Variational Bayes 无限Dirichlet混合模型及其变分贝叶斯应用

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.81

Wentao Fan, N. Bouguila

引用次数: 10

A New Method to Generate Virtual Samples for Solving Small Sample Set Problems 一种求解小样本集问题的虚拟样本生成新方法

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.18

A. Dehghani, Jun Zheng

引用次数: 4

Regional Normal Liver Tissue Density Changes in Patients Treated with Stereotactic Body Radiation Therapy for Liver Metastases 立体定向放射治疗肝转移患者局部正常肝组织密度的变化

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.121

C. Howells, Q. Diot, D. Westerly, M. Miften

{"title":"Regional Normal Liver Tissue Density Changes in Patients Treated with Stereotactic Body Radiation Therapy for Liver Metastases","authors":"C. Howells, Q. Diot, D. Westerly, M. Miften","doi":"10.1109/ICMLA.2011.121","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.121","url":null,"abstract":"A quantitative approach to evaluate stereo tactic body radiation therapy (SBRT)-induced normal liver tissue changes in patients with liver metastases was performed. 104 non-contrast treatment follow-up computed topography (CT) scans of 35 patients who received SBRT between 2004 and 2011 were retrospectively analyzed (range, 0.7-36 months, median, 8.1 months). The dose distributions from planning CTs were mapped to follow-up CTs using rigid registration. SBRT-induced normal liver density changes on post-SBRT follow-up CT scans were evaluated at approximately 4, 8, 12, 18, and 36 months. Dose-response curves (DRCs) were generated over the entire patient population by computing the mean Hounsfield unit (HU) in liver regions corresponding to dose bins ranging from 0-55 Gy in 5 Gy intervals. A hypo dense radio logic change in irradiated liver linearly related to dose (slope, -0.13 ÄHU/Gy) was observed, with significant mean CT changes of-9.3 ± 0.64 ÄHU and-9.8 ± 0.75 ÄHU at 45-50 Gy and 50-55 Gy, respectively. Furthermore, the data revealed that SBRT induces this hypo dense radiation reaction with demarcation set by the 30 to 35 Gy iso dose volume.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"2 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":"130855523","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

Deep Transfer Learning via Restricted Boltzmann Machine for Document Classification 基于受限玻尔兹曼机的深度迁移学习文档分类

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.51

Jian Zhang

{"title":"Deep Transfer Learning via Restricted Boltzmann Machine for Document Classification","authors":"Jian Zhang","doi":"10.1109/ICMLA.2011.51","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.51","url":null,"abstract":"Transfer learning aims to improve a targeted learning task using other related auxiliary learning tasks and data. Most current transfer-learning methods focus on scenarios where the auxiliary and the target learning tasks are very similar: either (some of) the auxiliary data can be directly used as training examples for the target task or the auxiliary and the target data share the same representation. However, in many cases the connection between the auxiliary and the target tasks can be remote. Only a few features derived from the auxiliary data may be helpful for the target learning. We call such scenario the deep transfer-learning scenario and we introduce a novel transfer-learning method for deep transfer. Our method uses restricted Boltzmann machine to discover a set of hierarchical features from the auxiliary data. We then select from these features a subset that are helpful for the target learning, using a selection criterion based on the concept of kernel-target alignment. Finally, the target data are augmented with the selected features before training. Our experiment results show that this transfer method is effective. It can improve classification accuracy by up to more than 10%, even when the connection between the auxiliary and the target tasks is not apparent.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"1 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":"131694525","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: 26