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2012 11th International Conference on Machine Learning and Applications最新文献

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Balancing Public Cycle Sharing Schemes Using Independent Learners 使用独立学习者平衡公共自行车共享方案
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.36
Jeremiah Smith, Luke Dickens, K. Broda
{"title":"Balancing Public Cycle Sharing Schemes Using Independent Learners","authors":"Jeremiah Smith, Luke Dickens, K. Broda","doi":"10.1109/ICMLA.2012.36","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.36","url":null,"abstract":"This paper concerns the resource management problem arising in public cycle sharing schemes, when some docking stations become empty and remain so while others fill to capacity. To alleviate this, managing companies move bicycles between docking stations in order to maximise the number of satisfied customers while minimising the movement cost. We identify Reinforcement learning (RL) as the most promising technique for finding good movement strategies in these networks, but conventional function-approximation RL methods do not scale well here, due to the quadratic growth in number of actions with network size. We propose the use of cooperating agents, namely Independent Learners, to partition the action space. To overcome the well known issue of coordination in Independent Learners, we combine a novel scheduling approach for asynchronous learning, with a modified Gradient-descent Sarsa(λ) algorithm to manage variable step-sizes. Our method competes with, and scales more favourably than, single-agent RL on a selection of simulated networks.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"1 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":"134066381","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
Can Frequent Itemset Mining Be Efficiently and Effectively Used for Learning from Graph Data? 频繁项集挖掘能有效地用于图数据学习吗?
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.74
Thashmee Karunaratne, Henrik Boström
{"title":"Can Frequent Itemset Mining Be Efficiently and Effectively Used for Learning from Graph Data?","authors":"Thashmee Karunaratne, Henrik Boström","doi":"10.1109/ICMLA.2012.74","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.74","url":null,"abstract":"Standard graph learning approaches are often challenged by the computational cost involved when learning from very large sets of graph data. One approach to overcome this problem is to transform the graphs into less complex structures that can be more efficiently handled. One obvious potential drawback of this approach is that it may degrade predictive performance due to loss of information caused by the transformations. An investigation of the tradeoff between efficiency and effectiveness of graph learning methods is presented, in which state-of-the-art graph mining approaches are compared to representing graphs by itemsets, using frequent itemset mining to discover features to use in prediction models. An empirical evaluation on 18 medicinal chemistry datasets is presented, showing that employing frequent itemset mining results in significant speedups, without sacrificing predictive performance for both classification and regression.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"179 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":"134368758","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
Occluded Face Recognition Using Correntropy-Based Nonnegative Matrix Factorization 基于相关熵的非负矩阵分解遮挡人脸识别
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.112
T. Ensari, J. Chorowski, J. Zurada
{"title":"Occluded Face Recognition Using Correntropy-Based Nonnegative Matrix Factorization","authors":"T. Ensari, J. Chorowski, J. Zurada","doi":"10.1109/ICMLA.2012.112","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.112","url":null,"abstract":"Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function. In this paper we propose to maximize the correntropy similarity measure as an objective function for NMF. Correntropy has been recently defined as a nonlinear similarity measure using an entropy-based criterion. After the minimization process of the correntropy function, we use it to recognize occluded face data set and compare its recognition performance with the standard NMF and Principal Component Analysis (PCA). The experimental results are illustrated with ORL face data set. The results show that our correntropy-based NMF (NMF-Corr) has better recognition rate compared with PCA and NMF.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"1 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":"131570071","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: 9
Large Scale URL-based Classification Using Online Incremental Learning 基于在线增量学习的大规模url分类
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.199
Nidhi Singh, Harsimrat Sandhawalia, Nicolas Monet, H. Poirier, Jean-Marc Coursimault
{"title":"Large Scale URL-based Classification Using Online Incremental Learning","authors":"Nidhi Singh, Harsimrat Sandhawalia, Nicolas Monet, H. Poirier, Jean-Marc Coursimault","doi":"10.1109/ICMLA.2012.199","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.199","url":null,"abstract":"We address the problem of large-scale topic classification of web pages based on the minimal text available in the URLs. This problem is challenging because of the sparsity of feature vectors that are derived from the URL text, and the typical asymmetry between the cardinality of train and test sets due to non-availability of sufficient sets of annotated URLs for training and very large test sets (e.g., in the case of large-scale focused crawling). We propose an online incremental learning algorithm which addresses these issues. Our experiments based on large publicly available datasets demonstrate an improvement of 0.11 -- 0.12 in terms of F-measure over the baseline algorithms, like Support Vector Machine, in difficult scenarios where the cardinality of train set is just a fraction of that of the test set.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"70 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":"131583196","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: 20
A Game Theoretic Framework for Communication in Fully Observable Multiagent Systems 完全可观察多智能体系统通信的博弈论框架
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.130
T. Reddy, G. Záruba, M. Huber
{"title":"A Game Theoretic Framework for Communication in Fully Observable Multiagent Systems","authors":"T. Reddy, G. Záruba, M. Huber","doi":"10.1109/ICMLA.2012.130","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.130","url":null,"abstract":"Communication is an important element of multiagent systems (MAS). In fully decentralized systems it is needed to allow the agents to coordinate their actions to achieve certain goals. When the agents have no means to coordinate their actions, they generally choose actions that minimize their chance of losses. If the agents were allowed to coordinate, on the other hand, they can choose actions that allow them to get higher rewards instead. In game theory, these concepts are known as risk dominance and payoff dominance. In this paper, we model communication between agents in stochastic games as an extensive form game in which the agents can numerically evaluate the benefit of communicating a piece of information to the other agents. This allows the agent to answer two important questions about the communication process, namely when should an agent communicate, and what should an agent communicate. We also present a more stable way to select between Nash equilibria in multiagent reinforcement learning using NashQ which is used in the calculation of the values for the communication game.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"1 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":"129382827","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
Unsupervised Anomaly Detection in Transactional Data 事务性数据中的无监督异常检测
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.96
M. Bouguessa
{"title":"Unsupervised Anomaly Detection in Transactional Data","authors":"M. Bouguessa","doi":"10.1109/ICMLA.2012.96","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.96","url":null,"abstract":"We propose a systematic approach to identify outlier in transactional data. First, we define a measure to estimate an outlying score for each transaction. Then, based on the estimated scores, we propose a probabilistic method that exploits the beta mixture model to automatically identify outliers. In contrast to existing transactional outlier detection methods, the approach that we propose does not require the target number of outliers in the data. Furthermore, our method is able to automatically discriminate between outliers and inliers without requiring any user-predefined threshold. Experiments on both synthetic and real data demonstrate the superior performance of our approach.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"57 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":"131199599","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
Prediction of Protein Structures Using GPU Based Simulated Annealing 基于GPU模拟退火的蛋白质结构预测
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.117
Hui Li, Chunmei Liu
{"title":"Prediction of Protein Structures Using GPU Based Simulated Annealing","authors":"Hui Li, Chunmei Liu","doi":"10.1109/ICMLA.2012.117","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.117","url":null,"abstract":"Simulated annealing (SA) is one of the popular approaches to predict protein structures. SA is prohibitive because it usually consumes much computing time and is likely to fall into local minimum points. We proposed a parallel SA algorithm based on a Graph Process Unit (GPU) technique to improve the efficiency and accuracy of the protein structure prediction. First, we analyze the SA algorithm based on CPU, second, we introduce the architecture of Compute Unified Device Architecture (CUDA). Finally, we applied statistical method to optimize the performance of the CUDA based parallel algorithm. The experimental result shows that our algorithm provides a feasible solution for the protein structure prediction.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"15 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":"132590395","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
The Class-Imbalance Problem for High-Dimensional Class Prediction 高维类预测中的类不平衡问题
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.223
L. Lusa, R. Blagus
{"title":"The Class-Imbalance Problem for High-Dimensional Class Prediction","authors":"L. Lusa, R. Blagus","doi":"10.1109/ICMLA.2012.223","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.223","url":null,"abstract":"The goal of class prediction studies is to develop rules to accurately predict the class membership of new subjects. The classifiers differ in the way they combine the values of the variables available for each subject. Frequently the classifiers are developed using class-imbalanced data, where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data are often biased towards the majority class: they classify most new samples in the majority class and they do not accurately predict the minority class. Data are high-dimensional when the number of variables greatly exceeds the number of subjects. In this paper we show how the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. Here we present new simulation studies for five classifiers, where we expand our previous results to correlated variables, and briefly discuss the results.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"12 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":"133126174","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
An Inverse Reinforcement Learning Algorithm for Partially Observable Domains with Application on Healthcare Dialogue Management 部分可观察域的逆强化学习算法及其在医疗对话管理中的应用
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.31
H. Chinaei, B. Chaib-draa
{"title":"An Inverse Reinforcement Learning Algorithm for Partially Observable Domains with Application on Healthcare Dialogue Management","authors":"H. Chinaei, B. Chaib-draa","doi":"10.1109/ICMLA.2012.31","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.31","url":null,"abstract":"In this paper, we propose an algorithm for learning a reward model from an expert policy in partially observable Markov decision processes (POMDPs). The problem is formulated as inverse reinforcement learning (IRL) in the POMDP framework. The proposed algorithm then uses the expert trajectories to find an unknown reward model-based on the known POMDP model components. Similar to previous IRL work in Markov Decision Processes (MDPs), our algorithm maximizes the sum of the margin between the expert policy and the intermediate candidate policies. However, in contrast to previous work, the expert and intermediate candidate policy values are approximated using the beliefs recovered from the expert trajectories, specifically by approximating expert belief transitions. We apply our IRL algorithm to a healthcare dialogue POMDP where the POMDP model components are estimated from real dialogues. Our experimental results show that the proposed algorithm is able to learn a reward model that accounts for the expert policy.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"61 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":"123919478","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: 11
Enhanced multiagent multi-objective reinforcement learning for urban traffic light control 基于多智能体多目标强化学习的城市交通灯控制
2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.108
Mohamed A. Khamis, W. Gomaa
{"title":"Enhanced multiagent multi-objective reinforcement learning for urban traffic light control","authors":"Mohamed A. Khamis, W. Gomaa","doi":"10.1109/ICMLA.2012.108","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.108","url":null,"abstract":"Traffic light control is one of the major problems in urban areas. This is due to the increasing number of vehicles and the high dynamics of the traffic network. Ordinary methods for traffic light control cause high rate of accidents, waste in time, and affect the environment negatively due to the high rates of fuel consumption. In this paper, we develop an enhanced version of our multiagent multi-objective traffic light control system that is based on a Reinforcement Learning (RL) approach. As a testbed framework for our traffic light controller, we use the open source Green Light District (GLD) vehicle traffic simulator. We analyze and fix some implementation problems in GLD that emerged when applying a more realistic continuous time acceleration model. We propose a new cooperation method between the neighboring traffic light agent controllers using specific learning and exploration rates. Our enhanced traffic light controller minimizes the trip time in major arteries and increases safety in residential areas. In addition, our traffic light controller satisfies green waves for platoons traveling in major arteries and considers as well the traffic environmental impact by keeping the vehicles speeds within the desirable thresholds for lowest fuel consumption. In order to evaluate the enhancements and new methods proposed in this paper, we have added new performance indices to GLD.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"3 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":"114958623","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: 47
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