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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是101-110 订阅

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Statistical supports for frequent itemsets on data streams

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4th international conference on machine learning and data mining in pattern recognition, mldm 2005

作者： Laur, Pierre-Alain Symphor, Jean-Emile Nock, Richard Poncelet, Pascal GRIMAAG-Dépt Scientifique Interfacultaire Université Antilles-Guyane Campus de Schoelcher B.P. 7209 97275 Schoelcher Cedex Martinique France LG2IP-Ecole des Mines d'Alès Site EERIE parc scientifique Georges Besse 30035 Nîmes Cedex France

ISBN: (纸本)3540269231

When we mine information for knowledge on a whole data streams it's necessary to cope with uncertainty as only a part of the stream is available. We introduce a stastistical technique, independant from the used algorithm, for estimating the frequent itemset on a stream. this statistical support allows to maximize either the precision or the recall as choosen by the user, while it doesn't damage the other. Experiments with various association rules databases demonstrate the potential of such technique. © Springer-Verlag Berlin Heidelberg 2005.

关键词： data reduction

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Cost integration in multi-step viewpoint selection for object recognition

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Derichs, C Deinzer, F Niemann, H Univ Erlangen Nurnberg Dept Comp Sci Chair Pattern Recognit D-91058 Erlangen Germany Siemens Med Solut D-91301 Forchheim Germany

ISBN: (纸本)3540269231

During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order to acquire just the most significant views of an arbitrary object. Although fusion of multiple views can already be done reliably, planning is still limited to gathering the next best view, normally the one providing the highest immediate gain in information. In this paper, we show how to perform a generally more intelligent, long-run optimized sequence of actions by linking them with costs. therefore it will be introduced how to acquire the cost of an appropriate dimensionality in a non-empirical way while still leaving the determination of the system's basic behavior to the user. Since this planning process is accomplished by an underlying machine learning technique, we also point out the ease of adjusting these to the expanded task and show why to use a multi-step approach for doing so.

关键词： viewpoint selection active vision reinforcement learning

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Incremental classification rules based on association rules using formal concept analysis

引用

4th international conference on machine learning and data Minining in pattern recognition

作者： Gupta, A Kumar, N Bhatnagar, V Univ Delhi Dept Comp Sci Delhi 110007 India

ISBN: (纸本)3540269231

Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. this gives more accurate classifiers for Classification. the algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. the incremental behavior is very useful in finding classification rules for real time data such as image processing. the algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.

关键词： classification rules formal concept analysis data mining concept lattice

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Temporal approach to association rule mining using T-tree and P-tree

引用

4th international conference on machine learning and data mining in pattern recognition, mldm 2005

作者： Verma, Keshri Vyas, O.P. Vyas, Ranjana School of Studies in Computer Science Pt. Ravishankar Shukla University Raipur Chhattisgarh - 49201 India

ISBN: (纸本)3540269231

the real transactional databases often exhibit temporal characteristic and time varying behavior. Temporal association rule has thus become an active area of research. A calendar unit such as months and days, clock units such as hours and seconds and specialized units such us business days and academic years, play a major role in a wide range of information system applications. the calendar-based pattern has already been proposed by researchers to restrict the time-based associationships. this paper proposes a novel algorithm to find association rule on time dependent data using efficient T-tree and P-tree data structures. the algorithm elaborates the significant advantage in terms of time and memory while incorporating time dimension. Our approach of scanning based on time-intervals yields smaller dataset for a given valid interval thus reducing the processing time. this approach is implemented on a synthetic dataset and result shows that temporal TFP tree gives better performance over a TFP tree approach. © Springer-Verlag Berlin Heidelberg 2005.

关键词： data mining

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A grouping method for categorical attributes having very large number of values

引用

4th international conference on machine learning and data Minining in pattern recognition

作者： Boullé, M France Telecom R&D F-22300 Lannion France

ISBN: (纸本)3540269231

In supervised machine learning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In case of very large number of values, the risk of overfilling the data increases sharply and building good groupings becomes difficult. In this paper, we propose two new grouping methods founded on a Bayesian approach, leading to Bayes optimal groupings. the first method exploits a standard schema for grouping models and the second one extends this schema by managing a "garbage" group dedicated to the least frequent values. Extensive comparative experiments demonstrate that the new grouping methods build high quality groupings in terms of predictive quality, robustness and small number of groups.

关键词： learning systems

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Ranked modelling with feature selection based on the CPL criterion functions

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4th international conference on machine learning and data mining in pattern recognition

作者： Bobrowski, L Bialystok Tech Univ Fac Comp Sci Bialystok Poland PAS Inst Biocybernet & Biomed Engn Wroclaw Poland

ISBN: (纸本)3540269231

Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not important for preserving the vectors order should be neglected. this way unimportant dimensions are greatly reduced in the feature space. It is particularly important in the case of "long" feature vectors, when a relatively small number of objects is represented in a high dimensional feature space, in the paper, we describe designing ranked models with the feature selection which is based on the minimisation of convex and piecewise linear (CPL) functions.

关键词： ranked linear models feature selection convex and piecewise linear (CPL) criterion functions linear separability of data sets

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mining expressive temporal associations from complex data

引用

4th international conference on machine learning and data mining in pattern recognition, mldm 2005

作者： Pray, Keith A. Ruiz, Carolina BAE Systems Burlington MA 01803 United States Worcester MA 01609 United States

ISBN: (纸本)3540269231

We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. data sets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment [1] and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set. © Springer-Verlag Berlin Heidelberg 2005.

关键词： data mining

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Unsupervised learning of visual feature hierarchies

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Scalzo, F Piater, J Univ Liege Inst Montefiore B-4000 Liege Belgium

ISBN: (纸本)3540269231

We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation-Maximization to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. this learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Belief Propagation. Each message is represented by a Gaussian mixture where each component represents a possible location of the feature. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view point changes.

关键词： Feature extraction

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An integrated approach for mining meta-rules

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Ye, FY Wang, JD Wu, SL Chen, HP Huang, TQ Tao, L Nanjing Univ Aeronaut & Astronaut Coll Informat Sci & Technol Nanjing 210016 Peoples R China Jiangsu Teachers Coll Technol Dept Comp Sci & Technol Changzhou 213001 Peoples R China

ISBN: (纸本)3540269231

An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. then every projected database is scanned to construct a hyper-structure. through mining the hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. the experimental results show that our approach is very promising.

关键词： data mining

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Principles of multi-kernel data mining

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4th international conference on machine learning and data mining in pattern recognition

作者： Mottl, V Krasotkina, O Seredin, O Muchnik, I Russian Acad Sci Ctr Comp Moscow 117968 Russia Rutgers State Univ DIMACS Piscataway NJ 08855 USA

ISBN: (纸本)3540269231

the scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for dissimilarity-based processing of data in metric spaces representing distances between entities, for which sufficiently informative features cannot be found, is much poorer. In this work, the problem of embedding the given set of entities into a linear space with inner product by choosing an appropriate kernel function is considered as the major challenge in the featureless approach to estimating dependences in data sets of arbitrary kind. As a rule, several kernels may be heuristically suggested within the bounds of the same data analysis problem. We treat several kernels on a set of entities as Cartesian product of the respective number of linear spaces, each supplied with a specific kernel function as a specific inner product. the main requirement here is. to avoid discrete selection in eliminating redundant kernels with the purpose of achieving acceptable computational complexity of the fusion algorithm.

关键词： data mining

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