2013 12th International Conference on Machine Learning and Applications最新文献_第10页

Linear Online Learning over Structured Data with Distributed Tree Kernels 基于分布式树核的结构化数据线性在线学习

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.28

Simone Filice, D. Croce, Roberto Basili, Fabio Massimo Zanzotto

{"title":"Linear Online Learning over Structured Data with Distributed Tree Kernels","authors":"Simone Filice, D. Croce, Roberto Basili, Fabio Massimo Zanzotto","doi":"10.1109/ICMLA.2013.28","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.28","url":null,"abstract":"Online algorithms are an important class of learning machines as they are extremely simple and computationally efficient. Kernel methods versions can handle structured data, such as trees, and achieve state-of-the-art performance. However kernelized versions of Online Learning algorithms slow down when the number of support vectors becomes large. The traditional way to cope with this problem is introducing budgets that set the maximum number of support vectors. In this paper, we investigate Distributed Trees (DT) as an efficient way to use structured data in online learning. DTs effectively embed the huge feature space of the tree fragments into small vectors, so enabling the use of linear versions of kernel machines over tree structured data. We experiment with the Passive-Aggressive (PA) algorithm by comparing the linear and the kernelized version. A massive dataset made with tree structured data is employed: it is originated from a natural language processing task, the Boundary Detection in the context of Semantic Role Labeling over Frame Net. Results on a sample of the final data show that the DTs along with the Linear PA algorithm and the Tree Kernel along with the Bundgeted PA achieve comparable results in terms of f1-measure. Finally, the exploration of the full dataset allows the former to improve the performance on the classification task, with respect to the latter.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123910991","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

A Robust Unsupervised Feature Learning Framework Using Spatial Boosting Networks 基于空间增强网络的鲁棒无监督特征学习框架

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.168

N. Le, M. Tran

{"title":"A Robust Unsupervised Feature Learning Framework Using Spatial Boosting Networks","authors":"N. Le, M. Tran","doi":"10.1109/ICMLA.2013.168","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.168","url":null,"abstract":"To boost up power of unsupervised feature learning and deep learning, there has been a great effort in optimizing network structure to learn more efficient high level features. It is crucial for a network to have a sufficient amount of learnable parameters yet still be able to capture in variances in data. In this paper, the authors propose spatial boosting networks, which employ convolutional feature learning networks as learning components. Each component in a network is assigned to a certain spatial region. This allows the network learn more adaptive features for each region. In order to make spatial boosting networks to capture relationship between regions of the visual field, we also propose convolutional pooling procedure. By expanding pooling scope into overlapping regions, we expect the features pooled in higher level to be more robust to noises and more invariant to transformation. Experiments show that using spatial boosting networks boosts up accuracy up to 3% from conventional approaches in standard datasets CIFAR and STL. Moreover, these results are competitive in comparison with other methods by using only a basic feature learning algorithm.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117193148","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: 1

An Artificial Neural Networks Based Software System for Improved Learning Experience 基于人工神经网络的改进学习体验软件系统

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.175

Utku Kose

引用次数: 3

Ordered Segment for Classification of Big Data 面向大数据分类的有序分段

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.54

A. Fatholahzadeh

引用次数: 1

Advanced Pattern Recognition Approach for Fault Diagnosis of Wind Turbines 风电机组故障诊断的高级模式识别方法

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.150

H. Toubakh, M. S. Mouchaweh, E. Duviella

{"title":"Advanced Pattern Recognition Approach for Fault Diagnosis of Wind Turbines","authors":"H. Toubakh, M. S. Mouchaweh, E. Duviella","doi":"10.1109/ICMLA.2013.150","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.150","url":null,"abstract":"The production of maximum amount of electrical power from wind requires the improvement of wind turbine reliability. The life duration and the good functioning of the wind turbine depend heavily on the reliability of its blades. Thus, a critical task is to detect and isolate faults, as fast as possible, and regain optimal functioning in the shortest time. In this paper, a pattern recognition approach is proposed for fault diagnosis of a wind turbine, in particular the pitch system composed of actuators and sensors. To achieve this task, feature and decision spaces have been defined. The aim of the pitch system is to adjust the pitch angle of a blade in order to optimize the generated electrical power according to the wind speed. Thus, a fault in the pitch system can reduce the wind turbine performance. Pitch system fault diagnosis is a challenging task because the pitch system feedback compensates the effect of the fault in the pitch actuator. In addition, the observation of the pitch actuator performance is very hard due to the strong variability of the wind speed. A wind turbine simulator is used to validate the performance of the proposed approach.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115726944","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

Learning Relevance of Web Resources across Domains to Make Recommendations 学习跨领域的网络资源相关性，提出建议

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.144

Julia Hoxha, P. Mika, Roi Blanco

{"title":"Learning Relevance of Web Resources across Domains to Make Recommendations","authors":"Julia Hoxha, P. Mika, Roi Blanco","doi":"10.1109/ICMLA.2013.144","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.144","url":null,"abstract":"Most traditional recommender systems focus on the objective of improving the accuracy of recommendations in a single domain. However, preferences of users may extend over multiple domains, especially in the Web where users often have browsing preferences that span across different sites, while being unaware of relevant resources on other sites. This work tackles the problem of recommending resources from various domains by exploiting the semantic content of these resources in combination with patterns of user browsing behavior. We overcome the lack of overlaps between domains by deriving connections based on the explored semantic content of Web resources. We present an approach that applies Support Vector Machines for learning the relevance of resources and predicting which ones are the most relevant to recommend to a user, given that the user is currently viewing a certain page. In real-world datasets of semantically-enriched logs of user browsing behavior at multiple Web sites, we study the impact of structure in generating accurate recommendations and conduct experiments that demonstrate the effectiveness of our approach.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123141350","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

Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk 基于贝叶斯最小风险的成本敏感信用卡欺诈检测

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.68

Alejandro Correa Bahnsen, Aleksandar Stojanovic, Djamila Aouada, B. Ottersten

引用次数: 151

Ensemble Feature Selection Methods for a Better Regularization of the Lasso Estimate in P >> N Gene Expression Datasets P >> N基因表达数据集Lasso估计更好正则化的集成特征选择方法

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.116

Adel Aloraini

{"title":"Ensemble Feature Selection Methods for a Better Regularization of the Lasso Estimate in P >> N Gene Expression Datasets","authors":"Adel Aloraini","doi":"10.1109/ICMLA.2013.116","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.116","url":null,"abstract":"The problem of variable selection from a large number of candidate predictors has recently been addressed in the machine learning of bioinformatics field. This is due to advances in high-throughput micro array techniques such as Affymetrix Gene Chips, and Illumina micro arrays that allow for studying thousands of genes in a single experiment. However, the resultant data from such genomic tools suffers from an p >> n problem, where the number of genes (p) to be examined is much larger than the number of samples (n). In such a model selection, the learning is considered hard, and the goal is to achieve accurate predictions from the inferred models alongside with their interpretability. Towards this goal, this work will experiment with feature selection methods and show how to improve the choice of the tuning parameter (s) in the lasso estimate feature selection method by adding an extra layer of filter feature selection methods to the lasso estimate path when learn from p n gene expression datasets. The results show that when the lasso estimate is ensemble with filter feature selection methods, the prediction accuracy for the chosen predictors for each targeted variable has improved.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532998","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: 4

Automatic Behavior Learning for Personalized Assisted Living Systems 个性化辅助生活系统的自动行为学习

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.191

E. Kańtoch, P. Augustyniak

引用次数: 2

Evolving Dynamic Forecasting Model for Foreign Currency Exchange Rates Using Plastic Neural Networks 基于塑性神经网络的外汇汇率动态预测演化模型

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.99

G. M. Khan, D. Nayab, S. Mahmud, H. Zafar

{"title":"Evolving Dynamic Forecasting Model for Foreign Currency Exchange Rates Using Plastic Neural Networks","authors":"G. M. Khan, D. Nayab, S. Mahmud, H. Zafar","doi":"10.1109/ICMLA.2013.99","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.99","url":null,"abstract":"This work explores developmental plasticity in neural networks for forecasting the trends in the daily foreign currency exchange rates. With this work we achieved an efficient artificial neural network (ANN) based dynamic prediction model that make use of the trends in the historical daily prices of the foreign currency to predict the future daily rates while modifying its structure with the trends. The plasticity in ANN is explored to achieve a prediction model that is computationally robust and efficient. The system performance analysis prove that the prediction model proposed is efficient, computationally cost effective and unique in terms of its least dependency on the amount of previous data required for the future prediction. The prediction model achieved accuracy as high as 98.852 percent, in predicting a single day's data from ten days data history, over a span of 1000 days (3 years). Further exploration demonstrated that when the problem domain for the network was changed to predict daily currency prices for multiple chunks of days a much better accuracy was achieved. This performance proved the robustness of the model proposed in this work for a modified problem domain.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125840381","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: 2