2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第4页

Sensor dynamics in high dimensional phase spaces via nonlinear transformations: Application to helicopter loads monitoring 基于非线性变换的高维相空间传感器动力学:在直升机载荷监测中的应用

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008691

J. J. Valdés, C. Cheung, Matthew Li

{"title":"Sensor dynamics in high dimensional phase spaces via nonlinear transformations: Application to helicopter loads monitoring","authors":"J. J. Valdés, C. Cheung, Matthew Li","doi":"10.1109/CIDM.2014.7008691","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008691","url":null,"abstract":"Accurately determining component loads on a helicopter is an important goal in the helicopter structural integrity field, with repercussions on safety, component damage, maintenance schedules and other operations. Measuring dynamic component loads directly is possible, but these measurement methods are costly and are difficult to maintain. While the ultimate goal is to estimate the loads from flight state and control system parameters available in most helicopters, a necessary step is understanding the behavior of the loads under different flight conditions. This paper explores the behavior of the main rotor normal bending loads in level flight, steady turn and rolling pullout flight conditions, as well as the potential of computational intelligence methods in understanding the dynamics. Time delay methods, residual variance analysis (gamma test) using genetic algorithms, unsupervised non-linear mapping and recurrence plot and quantification analysis were used. The results from this initial work demonstrate that there are important differences in the load behavior of the main rotor blade under the different flight conditions which must be taken into account when working with machine learning methods for developing prediction models.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182314","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

Batch linear least squares-based learning algorithm for MLMVN with soft margins 基于批处理线性最小二乘的软边MLMVN学习算法

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008147

E. Aizenberg, I. Aizenberg

{"title":"Batch linear least squares-based learning algorithm for MLMVN with soft margins","authors":"E. Aizenberg, I. Aizenberg","doi":"10.1109/CIDM.2014.7008147","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008147","url":null,"abstract":"In this paper, we consider a batch learning algorithm for the multilayer neural network with multi-valued neurons (MLMVN) and its soft margins variant (MLMVN-SM). MLMVN is a neural network with a standard feedforward organization based on the multi-valued neuron (MVN). MVN is a neuron with complex-valued weights and inputs/output located on the unit circle. Standard MLMVN has a derivative-free learning algorithm based on the error-correction learning rule. Recently, this algorithm was modified for MLMVN with discrete outputs by using soft margins (MLMVN-SM). This modification improves classification results when MLMVN is used as a classifier. Another recent development in MLMVN is the use of batch acceleration step for MLMVN with a single output neuron. Complex QR-decomposition was used to adjust the output neuron weights for all learning samples simultaneously, while the hidden neuron weights were adjusted in a regular way. In this paper, we merge the soft margins approach with batch learning. We suggest a batch linear least squares (LLS) learning algorithm for MLMVN-SM. We also expand the batch technique to multiple output neurons and hidden neurons. This new learning technique drastically reduces the number of learning iterations and learning time when solving classification problems (compared to MLMVN-SM), while maintaining the classification accuracy of MLMVN-SM.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126834243","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: 10

Experimental studies on indoor sign recognition and classification 室内标识识别与分类的实验研究

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008707

Zhen Ni, Si-Yao Fu, Bo Tang, Haibo He, Xinming Huang

引用次数: 7

Convex multi-task relationship learning using hinge loss 基于铰链损失的凸多任务关系学习

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008149

Anveshi Charuvaka, H. Rangwala

{"title":"Convex multi-task relationship learning using hinge loss","authors":"Anveshi Charuvaka, H. Rangwala","doi":"10.1109/CIDM.2014.7008149","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008149","url":null,"abstract":"Multi-task learning improves generalization performance by learning several related tasks jointly. Several methods have been proposed for multi-task learning in recent years. Many methods make strong assumptions about symmetric task relationships while some are able to utilize externally provided task relationships. However, in many real world tasks the degree of relatedness among tasks is not known a priori. Methods which are able to extract the task relationships and exploit them while simultaneously learning models with good generalization performance can address this limitation. In the current work, we have extended a recently proposed method for learning task relationships using smooth squared loss for regression to classification problems using non-smooth hinge loss due to the demonstrated effectiveness of SVM classifier in single task classification settings. We have also developed an efficient optimization procedure using bundle methods for the proposed multi-task learning formulation. We have validated our method on one simulated and two real world datasets and have compared its performance to competitive baseline single-task and multi-task methods.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803510","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 text categorization using a system of high-precision and high-recall models 使用高精度和高召回模型的自动文本分类系统

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008692

Dai Li, Y. Murphey

引用次数: 3

Using data mining to investigate interaction between channel characteristics and hydraulic geometry channel types 利用数据挖掘技术研究河道特征与水工几何河道类型之间的相互作用

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008706

Leong Lee, Gregory S. Ridenour

{"title":"Using data mining to investigate interaction between channel characteristics and hydraulic geometry channel types","authors":"Leong Lee, Gregory S. Ridenour","doi":"10.1109/CIDM.2014.7008706","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008706","url":null,"abstract":"Data was mined for the purpose of extracting data from an online source to compute and classify hydraulic geometry as well as providing additional data (channel stability, material, and evenness) for pattern discovery. Hydraulic geometry, the relationships between a stream's geometry (width and depth) and flow (velocity and discharge), is applicable to flood prediction, water resources management, and modeling point sources of pollution. Although data to compute hydraulic geometry and additional channel data are freely available online, a systematic data mining approach is seldom if ever used for classification of hydraulic geometry and discernment of regional trends encompassing multi-state areas. In this paper, a method for computing and classifying hydraulic geometry from mined channel flow and geometry data from several states was introduced. Additional channel characteristics (stability, evenness, and material) were also mined. Channels were mapped by stability and a scatterplot matrix revealed no anomalies in the hydraulic geometry of individual channel sections. To assess the quality of data output, statistical analyses were conducted to show that our mined data were comparable to data from the literature as indicated by Euclidean distances between multivariate means, histograms of frequency distributions of hydraulic exponents, and Spearman's rank order correlation applied to channel types. Channels exhibited significant interaction between stability and material, between stability and evenness, but not between material and evenness. Boundary lines through the classification diagram were effective in discriminating stability and material but not evenness.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835632","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

A density-based clustering of the Self-Organizing Map using graph cut 一种基于密度的自组织映射聚类方法

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008145

Leonardo Enzo Brito da Silva, J. A. F. Costa

引用次数: 3

MapReduce guided approximate inference over graphical models MapReduce在图形模型上引导近似推理

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008702

Ahsanul Haque, Swarup Chandra, L. Khan, M. Baron

{"title":"MapReduce guided approximate inference over graphical models","authors":"Ahsanul Haque, Swarup Chandra, L. Khan, M. Baron","doi":"10.1109/CIDM.2014.7008702","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008702","url":null,"abstract":"A graphical model represents the data distribution of a data generating process and inherently captures its feature relationships. This stochastic model can be used to perform inference, to calculate posterior probabilities, in various applications such as classification. Exact inference algorithms are known to be intractable on large networks due to exponential time and space complexity. Approximate inference algorithms are instead widely used in practice to overcome this constraint, with a trade off in accuracy. Stochastic sampling is one such method where an approximate probability distribution is empirically evaluated using various sampling techniques. However, these algorithms may still suffer from scalability issues on large and complex networks. To address this challenge, we have designed and implemented several MapReduce based distributed versions of a specific type of approximate inference algorithm called Adaptive Importance Sampling (AIS). We compare and evaluate the proposed approaches using benchmark networks. Experimental result shows that our approach achieves significant scaleup and speedup compared to the sequential algorithm, while achieving similar accuracy asymptotically.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126985769","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

A novel criterion for overlapping communities detection and clustering improvement 一种新的重叠群落检测和聚类改进准则

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008675

A. Berti, A. Sperduti, Andrea Burattin

引用次数: 2

Optimization of the type-1 and interval type-2 fuzzy integrators in Ensembles of ANFIS models for prediction of the Dow Jones time series 基于ANFIS模型的1型和区间2型模糊积分器在道琼斯时间序列预测中的优化

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008666

Jesus Soto, P. Melin, O. Castillo

引用次数: 2