2008 Seventh International Conference on Machine Learning and Applications最新文献

{"title":"Clustering for DNA Microarray Data Analysis with a Graph Cut Based Algorithm","authors":"Jia Song, Chunmei Liu, Yinglei Song, Junfeng Qu","doi":"10.1109/ICMLA.2008.25","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.25","url":null,"abstract":"Clustering is an important approach to the analysis of DNA microarray data. In this paper, we develop a new algorithm that can cluster DNA microarray data with a graph cut based algorithm. The algorithm can generate a list of clustering results with statistically significant likelihood. It can thus resolve the issue where a gene product may participate in different subsets of co-expressed genes. Our testing results on two biological sets showed that this approach can achieve improved clustering accuracy, compared with other clustering methods.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116202473","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}

{"title":"Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre 1H-MRS Database of Brain Tumors","authors":"P. Lisboa, E. Romero, A. Vellido, M. Julià-Sapé, C. Arús","doi":"10.1109/ICMLA.2008.20","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.20","url":null,"abstract":"The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely the class discrimination achieved by the classifier, is applied in this study to the analysis of an international, multi-centre 1H-MRS database of brain tumors. This combination yields results that are both intuitively interpretable and very accurate. The method as a whole remains simple enough as to allow its easy integration in existing medical decision support systems.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131633136","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}

{"title":"Relational Analysis for Consensus Clustering from Multiple Partitions","authors":"M. Lebbah, Younès Bennani, H. Benhadda","doi":"10.1109/ICMLA.2008.12","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.12","url":null,"abstract":"This paper deals with the problem of combining multiple clustering algorithms using the same data set to get a single consensus clustering. Our contribution is to formally define the cluster consensus problem as an optimization problem. to reach this goal, we propose an original existing algorithm but still relatively unknown method named relational analysis (RA). This method has several advantages among which we can quote: its low computational complexity, it does not require a number of clusters and does not neglect the weak clustering result. The unsupervised clustering consensus method implemented in this work is quite general. We evaluate the effectiveness of cluster consensus in three qualitatively different data sets. Promising results are provided in all three situations for synthetic as well as real data sets.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132702914","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}

{"title":"Making the Lipschitz Classifier Practical via Semi-infinite Programming","authors":"A. Stuhlsatz, H. Meier, A. Wendemuth","doi":"10.1109/ICMLA.2008.26","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.26","url":null,"abstract":"This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a generalization of the maximum margin concept from Hilbert to Banach spaces. In contrast to the support vector machine approach, our algorithm is free to use any finite family of continuously differentiable functions which linearly compose the decision function. Nevertheless, robustness properties are maintained due to a maximizing margin. To obtain a useful algorithm, the inherent difficult problem is formulated in a convex semi-infinite program. Using this new formulation, we develop a duality result enabling us to solve the original problem iteratively as a finite sequence of constrained quadratic programming problems over a convex hull of matrices. We compare the performance of the Lipschitz classifier algorithm with state-of-the-art machine learning methodologies using a benchmark data set as well as a data set randomly generated from Gaussian mixtures.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131793572","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}

{"title":"Incremental Learning for Multitask Pattern Recognition Problems","authors":"S. Ozawa, A. Roy","doi":"10.1109/ICMLA.2008.70","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.70","url":null,"abstract":"This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes by itself and to utilize the knowledge on the previous tasks for learning of new tasks. In addition, the learning model must acquire knowledge of multiple tasks incrementally without unexpected forgetting under the condition that not only tasks but also training samples are sequentially given. The proposed model is evaluated for two artificial MTPR problem. In the experiments, we verify that the proposed model can acquire and accumulate task knowledge very stably and the speed of knowledge acquisition for new tasks is enhanced by transferring knowledge.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134639113","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}

{"title":"Selecting Examples in Manifold Reduced Feature Space for Active Learning","authors":"Catarina Silva, B. Ribeiro","doi":"10.1109/ICMLA.2008.86","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.86","url":null,"abstract":"Nowadays machine learning are faced with an overload of data, both in terms of examples and features. Although recent algorithms, like support vector machines, can handle high dimensionality, it remains valuable to find smaller and more fitted spaces to perform learning tasks. We propose a twofold approach to tackle these high dimensionality issues in a text classification setting. First we use manifold learning as a pre-processing step to nonlinearly reduce the feature space. Second we use support vector machines to implement an active learning strategy, where the kernel trick is used to define the active examples. This approach deals with the high dimensionality both reducing the features and the number of examples needed to reach a desired performance.Results on a real-world benchmark corpus from Reuters and also on a reduced realistic version of the corpus show first the visualization capabilities of manifold learning and the performance improvement achieved with the active learning strategy.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127761437","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}

{"title":"Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques","authors":"Tong Lin, L. Cerviño, Xiaoli Tang, N. Vasconcelos, Steve B. Jiang","doi":"10.1109/ICMLA.2008.143","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.143","url":null,"abstract":"Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating and tracking. Gating is considered as the current state of the art for precise lung cancer radiotherapy, which irradiates the tumor when it moves into a predefined gating window. Tracking seems to be a next-generation technique, and it operates in a more aggressive fashion by following the tumor position with radiation beam in real time. Existing methods for gating and tracking often rely on observed motion patterns of external surrogates or implanted fiducial markers. However, external surrogates suffer from certain degrees of inaccuracy, and implanted fiducial markers are in limited uses due to the risk of pneumothorax. Therefore, direct tumor targeting techniques without implanting fiducial markers are desired. Previous studies in fluoroscopic markerless targeting are mainly based on template matching methods, which may fail when tumor boundary is unclear in fluoroscopic images. In this paper, we propose a novel framework of markerless gating and tracking based on machine learning algorithms. Specifically, gating is treated as a two-class classification problem, which is solved by principal component analysis (PCA) and artificial neural network (ANN). Further, we formulate the tracking problem as a regression task, which employs the correlation between the tumor position and nearby surrogate anatomic features in the image. Four regression methods were tested in this study: 1-degree and 2-degree linear regression, artificial neural network (ANN), and support vector machine (SVM). Finally, we demonstrate the superb performance of the proposed markerless gating and tracking algorithms on 10 fluoroscopic image sequences of 9 patients. For gating, the target coverage (the precision) ranges from 90% to 99%, with mean of 96.5%. For tracking, the mean localization error is about 2.1 pixels and the maximum error at 95% confidence level is about 4.6 pixels (pixel size is about 0.5 mm).","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134373368","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}

{"title":"Distributed Planning in Stochastic Games with Communication","authors":"A. Burkov, B. Chaib-draa","doi":"10.1109/ICMLA.2008.56","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.56","url":null,"abstract":"This paper treats the problem of distributed planning in general-sum stochastic games with communication when the model is known. Our main contribution is a novel, game theoretic approach to the problem of distributed equilibrium computation and selection. We show theoretically and via experiments that our approach, when adopted by all agents, facilitates an efficient distributed equilibrium computation and leads to a unique equilibrium selection in general-sum stochastic games with communication.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134146096","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}

{"title":"Optimization on a Novel Quantum Greedy Approach Based on Learning Strategy for Zero and One Knapsack Problem and Evaluation","authors":"Mir Shahriar Emami","doi":"10.1109/ICMLA.2008.100","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.100","url":null,"abstract":"Now a days probabilistic approach can be a convenient solution for solving many of the problems especially the problems with high time complexity like as knapsack problem. In this paper I present a new quantum computation approach based on learning strategy and greedy approach for solving the zero and one knapsack problem with polynomial time complexity about O (n) and as a result of evaluation of my presented approach I show that the probability of success of the given approach is low based on disentangled quantum registers and without learning the past observations. Nevertheless I show that how learning approach can significantly increases the probability of success of the given quantum greedy method. Also as an another result of evaluation of the presented approach I show that if the standard deviation of the items increases, the probability of success of the given new learning approach increases too.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133773987","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}

{"title":"New Insights into Learning Algorithms and Datasets","authors":"Jun won Lee, C. Giraud-Carrier","doi":"10.1109/ICMLA.2008.8","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.8","url":null,"abstract":"We report on three distinct experiments that provide new valuable insights into learning algorithms and datasets. We first describe two effective meta-features that significantly impact the predictive accuracy of a broad range of learning algorithms. We then introduce a new efficient meta-feature that measures the degree of hardness (or difficulty) of datasets and show that it is highly linearly correlated with predictive accuracy. Finally, we use the notion of classifier output difference to cluster learning algorithms and show that learning algorithms from different model classes may demonstrate highly similar behaviors.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056140","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}