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2011 10th International Conference on Machine Learning and Applications and Workshops最新文献

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Role Model of Search in Agents' Parameter-Space agent参数空间中搜索的角色模型
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.124
O. Kazík, Roman Neruda
{"title":"Role Model of Search in Agents' Parameter-Space","authors":"O. Kazík, Roman Neruda","doi":"10.1109/ICMLA.2011.124","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.124","url":null,"abstract":"In this article we elaborate the formal model of roles in computational multi-agent systems (CMAS) in description logic. The CMAS model is enriched by a role-based model representing search (e.g. hill-climbing, genetic algorithms) in general search space. The choice of solution representation is important for successful and quick finding of the optimal solution. We apply the search model to optimization in the parameter space of data mining methods and employ it in a meta-learning scenario.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"45 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":"132669723","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
An Iris Recognition Approach based on Fuzzy Support Vector Machine 基于模糊支持向量机的虹膜识别方法
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.169
Hongying Gu, Zhiwen Gao, Cheng Yang
{"title":"An Iris Recognition Approach based on Fuzzy Support Vector Machine","authors":"Hongying Gu, Zhiwen Gao, Cheng Yang","doi":"10.1109/ICMLA.2011.169","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.169","url":null,"abstract":"An iris recognition system named IrisPassport is presented in this paper. Standard Deviation is used to localize the irises from iris images. After localization, IrisPassport uses Steerable Pyramid and Variant Fractal Dimension as features with orientation information. Aiming to build a robust solution for non-cooperative iris images, we adopt fuzzy support vector machine (FSVM) because we consider different samples contributes to classification differently and a member function can be used when unclassifiable regions appear. Experimental data demonstrates the potential of our new approach, and shows that it performs favorably when compared with the former algorithms.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"48 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":"134119729","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
Comparison of Classification Techniques used in Machine Learning as Applied on Vocational Guidance Data 机器学习分类技术在职业指导数据上的应用比较
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.49
H. Bulbul, Özkan Ünsal
{"title":"Comparison of Classification Techniques used in Machine Learning as Applied on Vocational Guidance Data","authors":"H. Bulbul, Özkan Ünsal","doi":"10.1109/ICMLA.2011.49","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.49","url":null,"abstract":"Recent developments in information systems as well as computerization of business processes by organizations have led to a faster, easier and more accurate data analysis. Data mining and machine learning techniques have been used increasingly in the analysis of data in various fields ranging from medicine to finance, education and energy applications. Machine learning techniques make it possible to deduct meaningful further information from those data processed by data mining. Such meaningful and significant information helps organizations to establish their future policies on a sounder basis, and to gain major advantages in terms of time and cost. This study applies classification algorithms used in data mining and machine learning techniques on those data obtained from individuals during the vocational guidance process, and tries to determine the most appropriate algorithm.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"58 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":"115162299","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: 35
Dimensionality Reduction by Unsupervised K-Nearest Neighbor Regression 无监督k近邻回归的降维方法
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.55
Oliver Kramer
{"title":"Dimensionality Reduction by Unsupervised K-Nearest Neighbor Regression","authors":"Oliver Kramer","doi":"10.1109/ICMLA.2011.55","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.55","url":null,"abstract":"In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UNN formulation allows the design of efficient strategies that iteratively embed latent points to fixed neighborhood topologies. UNN is well appropriate for sorting of high-dimensional data. The iterative variants are analyzed experimentally.","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":"128395392","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: 45
A Neural Network Model for Learning Data Stream with Multiple Class Labels 多类标签数据流学习的神经网络模型
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.16
Tomoyasu Takata, S. Ozawa
{"title":"A Neural Network Model for Learning Data Stream with Multiple Class Labels","authors":"Tomoyasu Takata, S. Ozawa","doi":"10.1109/ICMLA.2011.16","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.16","url":null,"abstract":"In this paper, we extend the sequential multitask learning model called Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR) proposed by Nishikawa et al. such that it can learn a training sample with multiple class labels which are originated from different lassification tasks. Here, we assume that no task information is given for training samples. Therefore, the extended RAN-MTPR has to allocate multiple class labels to appropriate tasks under unsupervised settings. This is carried out based on the prediction errors in the output sections, and the most probable task is selected from the output section with a minimum error. Through the computer simulations using the ORL face dataset, we show that the extended RAN-MTPR works well as a multitask learning model.","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":"128545688","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
Arabic Text-Dependent Speaker Verification for Mobile Devices Using Artificial Neural Networks 基于人工神经网络的移动设备阿拉伯语文本依赖说话人验证
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.168
A. Alarifi, Issa Alkurtass, A. Al-Salman
{"title":"Arabic Text-Dependent Speaker Verification for Mobile Devices Using Artificial Neural Networks","authors":"A. Alarifi, Issa Alkurtass, A. Al-Salman","doi":"10.1109/ICMLA.2011.168","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.168","url":null,"abstract":"Speaker verification is one of the biometric verification techniques used to verify the claimed identity of a speaker. It is mainly applied for security reasons and managing users' authentication. Voiceprint can be used as a unique password of the user to prove his/her identity. In this paper, we propose a new Arabic text-dependent speaker verification system for mobile devices using artificial neural networks (ANN) to recognize authorized user and unlock devices for him/her. We describe system components and demonstrate how it works. We present the performance of our system and analyze its results.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"32 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":"134116646","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
Solving the Traveling Salesman Problem through Iterative Extended Changing Crossover Operators 利用迭代扩展变换交叉算子求解旅行商问题
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.129
R. Takahashi
{"title":"Solving the Traveling Salesman Problem through Iterative Extended Changing Crossover Operators","authors":"R. Takahashi","doi":"10.1109/ICMLA.2011.129","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.129","url":null,"abstract":"The subject of the Genetic Algorithm (GA) is to devise methodologies that can efficiently locate the optimum solution which must satisfy the contrary requirements of preserving population diversity and speeding up convergence on the optimum solution. In this paper, a new hybrid method iterative Extended Changing Crossover Operators (i-ECXO) combining Edge Assembly Crossover (EAX) and Ant Colony Optimization (ACO) is proposed to solve the Traveling Salesman Problem (TSP). In EAX, parent A's edge EA is exchanged for parent B's edge EB finally to generate new off spring, where EA and EB alternate in forming so-called A-B cycles. ACO simulates swarm intelligence in ants' feeding behavior. In ACO, ants tend to create various kinds of cyclic paths with different sequences of visiting cities in early stage of generations. In this paper, the diversity of generations is strictly measured by the entropy H in Thermo Dynamical Genetic Algorithm (TDGA). With the measure H, the validity of i-ECXO is experimentally verified by using medium sized TSP data.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"121 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":"122132943","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
Improvement of Particle Filter for Reinforcement Learning 粒子滤波在强化学习中的改进
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.75
A. Notsu, Katsuhiro Honda, H. Ichihashi, Yuki Komori, Yuuki Iwamoto
{"title":"Improvement of Particle Filter for Reinforcement Learning","authors":"A. Notsu, Katsuhiro Honda, H. Ichihashi, Yuki Komori, Yuuki Iwamoto","doi":"10.1109/ICMLA.2011.75","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.75","url":null,"abstract":"In this paper, we propose a novel framework of learning that uses a particle filter. In a real-world situation, it is difficult to express a continuous state and a continuous action. The problem is solved by using our particle filter, which is one of the methods for dividing a continuous state and a continuous action. Our method needs only a small number of memories and parameters for searching the solution in the space. We conducted pendulum and double-pendulum simulations and observed the difference between the conventional method and the proposed method. Simulation results show there was no bad effect on the received reward.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"63 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":"126056651","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
An Experimental Study to Investigate the Use of Additional Classifiers to Improve Information Extraction Accuracy 利用附加分类器提高信息提取精度的实验研究
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.31
H. Lek, D. Poo
{"title":"An Experimental Study to Investigate the Use of Additional Classifiers to Improve Information Extraction Accuracy","authors":"H. Lek, D. Poo","doi":"10.1109/ICMLA.2011.31","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.31","url":null,"abstract":"In this paper, we present an information extraction system and investigate the use of additional classifiers to help improve information extraction performance. We propose a simple idea of training an additional classifier using the same feature configurations on another corpus and then using this new classifier to classify the original dataset. The classification result of this new classifier is then used as a feature to the original classifier. We tested this approach on the CMU seminar announcements and the Austin job posting datasets and obtained results better than all previously reported systems.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"13 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":"124770581","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
SVM Multi-classification of T2D/CVD Patients Using Biomarker Features 基于生物标志物特征的T2D/CVD患者SVM多分类
2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.182
S. Buddi, Thomas Taylor, C. Borges, R. Nelson
{"title":"SVM Multi-classification of T2D/CVD Patients Using Biomarker Features","authors":"S. Buddi, Thomas Taylor, C. Borges, R. Nelson","doi":"10.1109/ICMLA.2011.182","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.182","url":null,"abstract":"Cardiovascular disease (CVD) is considered as the leading cause of morbidity and mortality in type 2 diabetes (T2D) patients. In 2008 the US FDA issued a Guidance to Industry statement, recognizing the conjoined nature of CVD and T2D and emphasizing the need to monitor cardiovascular risk during new diabetic drug trials. This led researchers to work towards identifying panels of markers that are able to distinguish subtypes of CVD in the context of T2D. Immunoassays are used to detect and quantify biomolecules in a solution. Mass spectrometric immunoassay analysis of various proteins in the blood serum of 212 subjects belonging to multiple disease groups resulted in the identification of 41 molecular species as potential biomarkers. In this paper, support vector machines are used to measure the effectiveness of using these species as a diagnosis tool. We suggest an any-vs-rest SVM multiclass classification method by dividing the problem into a series of binary SVM classification problems and using a MAP decision rule to predict the correct class. One-vs-rest and discriminant analysis approaches are also evaluated for comparison.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"05 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":"127433626","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
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