2012 11th International Conference on Machine Learning and Applications最新文献_第10页

Sparse Representation Based Discriminative Canonical Correlation Analysis for Face Recognition 基于稀疏表示的判别典型相关分析人脸识别

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.18

Naiyang Guan, Xiang Zhang, Zhigang Luo, L. Lan

引用次数: 19

Abrupt and Drift-Like Fault Diagnosis of Concurent Discrete Event Systems 并发离散事件系统的突发性和漂移型故障诊断

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.157

M. S. Mouchaweh, P. Billaudel

引用次数: 8

Active Learning of Markov Decision Processes for System Verification 系统验证中马尔可夫决策过程的主动学习

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.158

Yingke Chen, Thomas D. Nielsen

{"title":"Active Learning of Markov Decision Processes for System Verification","authors":"Yingke Chen, Thomas D. Nielsen","doi":"10.1109/ICMLA.2012.158","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.158","url":null,"abstract":"Formal model verification has proven a powerful tool for verifying and validating the properties of a system. Central to this class of techniques is the construction of an accurate formal model for the system being investigated. Unfortunately, manual construction of such models can be a resource demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required to obtain accurate system models.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130863882","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: 21

Online Recovery of Missing Values in Vital Signs Data Streams Using Low-Rank Matrix Completion 利用低秩矩阵补全技术在线恢复生命体征数据流中的缺失值

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.55

Shiming Yang, K. Kalpakis, C. Mackenzie, L. Stansbury, D. Stein, T. Scalea, P. Hu

{"title":"Online Recovery of Missing Values in Vital Signs Data Streams Using Low-Rank Matrix Completion","authors":"Shiming Yang, K. Kalpakis, C. Mackenzie, L. Stansbury, D. Stein, T. Scalea, P. Hu","doi":"10.1109/ICMLA.2012.55","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.55","url":null,"abstract":"Continuous, automated, electronic patient vital signs data are important to physicians in evaluating traumatic brain injury (TBI) patients' physiological status and reaching timely decisions for therapeutic interventions. However, missing values in the medical data streams hinder applying many standard statistical or machine learning algorithms and result in losing some episodes of clinical importance. In this paper, we present a novel approach to filling missing values in streams of vital signs data. We construct sequences of Hankel matrices from vital signs data streams, find that these matrices exhibit low-rank, and utilize low-rank matrix completion methods from compressible sensing to fill in the missing data. We demonstrate that our approach always substantially outperforms other popular fill-in methods, like k-nearest-neighbors and expectation maximization. Further, we show that our approach recovers thousands of simulated missing data for intracranial pressure, a critical stream of measurements for guiding clinical interventions and monitoring traumatic brain injuries.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133654176","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: 16

A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data 一类偏斜数据的抗噪声增强算法

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.153

J. V. Hulse, T. Khoshgoftaar, Amri Napolitano

引用次数: 3

A Series Inspired CPG Model for Robot Walking Control 机器人行走控制的系列启发CPG模型

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.80

Jiaqi Zhang, Xianchao Zhao, Chenkun Qi

{"title":"A Series Inspired CPG Model for Robot Walking Control","authors":"Jiaqi Zhang, Xianchao Zhao, Chenkun Qi","doi":"10.1109/ICMLA.2012.80","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.80","url":null,"abstract":"Central pattern generator (CPG) is a kind of neural network which is located in the spinal cord. It has been found to be responsible for many rhythmic biological movements, such as breathing, swimming, flying as well as walking. Many CPG models have been designed and proved to be useful. But the CPG outputs of these models are often sine waves or quasi-sine waves. Also these outputs are directly used as the control signals to control joint trajectories or joint torques on robots. This is obviously not an accurate design in robot walking control especially when sine or quasisine waves are not the best signals to set walking patters because of the complexity of tasks. In this paper, based on the idea of Righetti, Buchli and Ijspeert, a CPG model is designed, which is inspired by Fourier series and can produce outputs with any shape. There are a limited set of sub-components in the proposed model. Each sub-component learns one harmonic of a reference wave. A summation of these sub-components is used to approximate the wave. In this way, the wave will be learned and embedded in the CPG model. In the proposed model, FFT is used to see the harmonics and calculate the frequency. The system is designed in polar coordinates with new Hebbian learning items and Kuramoto model items. Because the whole system is a limit cycle system, it is robust to perturbation. The experiment conducted on an AIBO robot shows the effectiveness of the proposed model.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724747","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: 3

Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology 遗传畸形诊断决策支持系统的建立

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.234

Kaya Kuru, M. Niranjan, Y. Tunca

{"title":"Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology","authors":"Kaya Kuru, M. Niranjan, Y. Tunca","doi":"10.1109/ICMLA.2012.234","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.234","url":null,"abstract":"In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support particularly when only very few previous examples exist or in a remote environment where expert knowledge is not readily accessible. In this work we develop and demonstrate that accurate classification of dysmorphic faces is feasible by image processing of two dimensional face images. We test the proposed system on real patient image data by constructing a dataset of dysmorphic faces published in scholarly journals, hence having accurate diagnostic information about the syndrome. Our statistical methodology represents facial image data in terms of principal component analysis (PCA) and a leave one out evaluation scheme to quantify accuracy. The methodology has been tested with 15 syndromes including 75 cases, 5 examples per syndrome. A diagnosis success rate of 79% has been established. It can be concluded that a great number of syndromes indicating a characteristic pattern of facial anomalies can be typically diagnosed by employing computer-assisted machine learning algorithms since a face develops under the influence of many genes, particularly the genes causing syndromes.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084955","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: 3

Polynomial Correlation Filters for Human Face Recognition 人脸识别的多项式相关滤波器

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.120

Mohamed I. Alkanhal, Muhammad Ghulam

引用次数: 3

Scalable Overlapping Co-clustering of Word-Document Data Word-Document数据的可扩展重叠共聚类

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.84

F. O. França

引用次数: 13

Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection 基于自适应非对称错误分类代价的SVM类地雷目标检测

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.227

Xiaoguang Wang, Hang Shao, N. Japkowicz, S. Matwin, Xuan Liu, A. Bourque, Bao Nguyen

引用次数: 14