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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2836 条记录，以下是2391-2400 订阅

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Comparison of a novel combined ECOC strategy with different multiclass algorithms together with parameter optimization methods 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Hillsmann, Marco Friedrich, Christoph M. Univ Cologne D-5000 Cologne 41 Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 Birlinghoven Germany

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper we consider multiclass learning tasks based on Support Vector machines (SVMs). In this regard, currently used methods are One-Against-All or One-Against-One, but there is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called Comb-ECOC, which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A problem with the usage of a multiclass method is the proper choice of parameters. Many users only take the default parameters of the respective learning algorithms (e.g. the regularization parameter C and the kernel parameter gamma). We tested different parameter optimization methods on different learning algorithms and confirmed the better performance of One-Against-One versus One-Against-All, which can be explained by the maximum margin approach of SVMs.

关键词： learning algorithms

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Feature extraction from microarray expression data by integration of semantic knowledge

Feature extraction from microarray expression data by integr...

引用

6th international conference on machine learning and Applications

作者： Cho, Young-Rae Xu, Xian Hwang, Woochang Zhang, Aidong SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)9780769530697

Microarray, techniques give biologists first peek into the molecular states of living tissues. Previous studies have proven that it is feasible to build sample classifiers using the gene expressional profiles. To build an effective sample classifier dimension reduction process is necessary since classic pattern recognition algorithms do not work well in high dimensional space. In this paper we present a novel feature extraction algorithm based on the concept of virtual genes by integrating microarray expression data sets with domain knowledge embedded in Gene Ontology (GO) annotations. We define semantic similarity to measure the functional associations between two genes using the annotation on each GO term. We then identify the groups of genes, called virtual genes, that potentially interact with each other for a biological function. the correlation in gene expression levels of virtual genes can be used to build a sample classifier For a colon cancer data set, the integration of microarray expression data with GO annotations significantly improves the accuracy of sample classification by more than 10%.

关键词： Classification (of information)

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A machine learning approach to test data generation: A case study in evaluation of gene finders 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Christiansen, Henning Dahmcke, Christina Mackeprang Roskilde Univ Dept Commun Business & Informat Technol Res Grp PLIS POB 260 DK-4000 Roskilde Denmark Roskilde Univ Dept Sci Syst & Models DK-4000 Roskilde Denmark

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. this has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). the quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrated these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and ***.

关键词： learning systems

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Discovering relations among entities from XML documents 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Wu, Yangyang Lei, Qing Luo, Wei Yokota, Harou Huaqiao Univ Dept Comp Sci Quanzhou Fujian Peoples R China Tokyo Inst Technol Dept Comp Sci Tokyo Japan

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper addresses relation information extraction problem and proposes a method of discovering relations among entities which is buried in different nest structures of XML documents. the method first identifies and collects XML fragments that contain all types of entities given by users, then computes similarity between fragments based on semantics of their tags and their structures, and clusters fragments by similarity so that the fragments containing the same relation are clustered together, finally extracts relation instances and patterns of their occurrences from each cluster. the results of experiments show that the method can identify and extract relation information among given types of entities correctly from all kinds of XML documents with meaningful tags.

关键词： relation information extraction XML document cluster occurrence pattern

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Study of clustering algorithm based on model data

Study of clustering algorithm based on model data

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6th international conference on machine learning and Cybernetics

作者： Li, Kai Cui, Li-Juan HeBei Univ Sch Math & Comp Baoding 071002 Peoples R China HeBei Univ Lib Baoding 071002 Peoples R China

ISBN: (纸本)9781424409723

Clustering technique is a key tool in data mining and pattern recognition. Usually, objects for some traditional clustering algorithms are expressed in the form of vectors, which consist of some components to be described as features. However, objects in real tasks may be some models which are clustered other than data points, for example! neural networks, decision trees, support vector machines, etc. this paper studies the clustering algorithm based on model data. By defining the extended measure, clustering methods are studied for the abstract data objects. Framework of clustering algorithm for models is presented. To validate the effectiveness of models clustering algorithm, we choose the hierarchical model clustering algorithm in the experiments. Models in clustering algorithm are BP(Back Propagation) neural networks and learning method is BP algorithm. Measures are chosen as both same-fault measure and double-fault measure for pairwise of models. Distances between clusters are the single link and the complete link, respectively. By this way, we may obtain part of neural network models which are from each cluster and improve diversity of neural network models. then, part of models is ensembled. Moreover, we also study the relations between the number of clusters in clustering analysis, the size of ensemble learning, and performance of ensemble learning by experiments. Experimental results show that performance of ensemble learning by choosing part of models using clustering of models is improved.

关键词： model clustering measure space validation of clustering diversity

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mining association rules from XML data with index table

Mining association rules from XML data with index table

引用

6th international conference on machine learning and Cybernetics

作者： Li, Xin-Ye Yuan, Jin-Sha Kong, Ying-Hui N China Elect Power Univ Dept Elect & Commun Engn Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9781424409723

mining XML association rule is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. In order to make mining XML association rule efficiently, we give a new definition of transaction and item in XML context, then build transaction database based on an index table. Based on our definition and the index table used for XML searching, we can check the include relation between a transaction and an item quickly. A high adaptive mining technique is also described. By using it, we can process mining rules with no guidance of interest associations given by users and mining unknown rules. We demonstrate the effectiveness of these techniques through experiments on real-life data.

关键词： XML mining association rule index table include relation

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LEONARDO - the computational intelligence (CI) model selection wizard

LEONARDO - The computational intelligence (CI) model selecti...

引用

6th international conference on machine learning and Applications

作者： Owotoki, Peter Mayer-Lindenberg, Friedrich TUHH Inst Comp Technol Hamburg Germany

ISBN: (纸本)9780769530697

the need for tools to aid the selection of the CI models that lie at the heart of many AI systems has never been greater, due to the mainstreaming of data mining and other AI applications. LEONARDO -our contribution to this process- is a recommender system that selects and ranks applicable CI models for a given problem based on the peculiarities of the domain as determined by the user's preferences and dataset characteristics. Leonardo's recommendations are based on two knowledge bases. One contains the description of 65 CI models and provides the Meta knowledge for pruning the space of all CI models to only those applicable to the current task. the second KB contains the performance results of over 200 datasets on the applicable CI models. LEONARDO's ranking is achieved by using the performance information of the k entries, from this KB, nearest in similarity to the new domain dataset.

关键词： data mining

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Multi-agent system approach to react to sudden environmental changes 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Raudys, Sarunas Mitasiunas, Antanas Inst Math & Informat Akad 4 LT-08663 Vilnius Lithuania Vilnius Univ Vilnius Lithuania

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Many processes experience abrupt changes in their dynamics. this causes problems for some prediction algorithms which assume that the dynamics of the sequence to be predicted are constant, or at least only change slowly over time. In this paper the problem of predicting sequences with sudden changes in dynamics is considered. For a model of multivariate Gaussian data we derive expected generalization error of standard linear Fisher classifier in situation where after unexpected task change, the classification algorithm learns on a mixture of old and new data. We show both analytically and by an experiment that optimal length of learning sequence depends on complexity of the task, input dimensionality, on the power and periodicity of. the changes. the proposed solution is to consider a collection of agents, in this case non-linear single layer perceptrons (agents), trained by a memetic like learning algorithm. T e most successful agents are voting for predictions. A grouped structure of the agent population assists in obtaining favorable diversity in the agent population. Efficiency of socially organized evolving multi-agent system is demonstrated on an artificial problem.

关键词： generalization dimensionality evolution learning multi-agent systems neural networks sample size

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Efficient computation of mahalanobis distance in financial hand-written Chinese character recognition

Efficient computation of mahalanobis distance in financial h...

引用

6th international conference on machine learning and Cybernetics

作者： Chen, Guang Zhang, Hong-Gang Guo, Jun Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781424409723

the recognition of hand-written Chinese characters using Mahalanobis distance is extensively utilized in bank cheque processing applications. the Mahalanobis distance, defined by the innovation and its covariance, is compared among several target character classes, and the computation is a time-consuming operation. this paper presents an efficient computation for this process. the method described here can be summarized as an incremental, non-decreasing computation for the Mahalanobis distance;if the incrementally computed value exceeds the threshold then the computation is stopped. the elements of covariance and innovation are only computed if they are used, and progressivity is the major advantage of the method. this method is based upon the square-root-free Cholesky's factorization. Experiment shows the method proposed here is effective in financial hand-written character recognition.

关键词： mahalanobis distance pattern recognition financial Chinese character recognition bank cheque processing

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Personality mining system in E-learning by using improved association rules

Personality mining system in E-learning by using improved as...

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6th international conference on machine learning and Cybernetics

作者： Yin, Chun-Yong Luo, Qi Binzhou Coll Dept Comp Sci Binzhou 256600 Peoples R China Wuhan Univ Sci & Technol Informat Engn Sch Wuhan 430223 Peoples R China

ISBN: (纸本)9781424409723

To meet the personalized needs of E-learning, an improved association mining rules was proposed in the paper. First, data cube from database was established. then, frequent item-set that satisfies the minimum support on data cube was mined out. Furthermore, association rules of frequent item-set was generated. Finally, redundant association rules through the relative method in statistics were wiped off. the algorithm had two advantages, the first was that the execution time was short while searching for the frequent item-set;the second was that the precision of the rules was high. the algorithm was also used in personality mining system based on E-learning model (PMSEM). the result manifested that the algorithm was effective.

关键词： E-learning mining algorithm association rules personality

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