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检索条件"任意字段=10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009"

共 105 条记录，以下是31-40 订阅

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Fuzzy Quantification-Based Linguistic Summaries in data Cubes with Hierarchical Fuzzy Partition of Time Dimension

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Castillo-Ortega, Rita Marin, Nicolas Sanchez, Daniel Univ Granada Intelligent Databases & Informat Syst Res Grp Dept Comp Sci & AI E-18071 Granada Spain

ISBN: (纸本)9783642043932

data Cubes are the basic structure of the Multi-dimensional data Model. OnLine Analytical Processing techniques together with data cubes come to overcome the limitation of conventional database models whose structures are not well suited for the friendly ad-hoc analysis and display of large amounts of data decision support systems need. One of the dimensions that usually appears in data cubes is the time dimensions. the use of OnLine Analytical Processing operations through this dimension produces its result time series data that, ask for suitable summarization techniques in order to effectively present the information to the interested user. Soft Computing approaches to data summarization are widely used to carry out this task. In this paper, we introduce ail approach to linguistic summarization of data in data cubes with time dimension using frizzy quantified statements. Our approach uses as basis a time dimension defined by the user as a hierarchical collection of fuzzy time periods.

关键词： Linguistic summarization Time series Dimensional data model OLAP Business Intelligence Fuzzy Logic

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the Minimum Redundancy - Maximum Relevance Approach to Building Sparse Support Vector Machines

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Yang, Xiaoxing Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Peoples R China

ISBN: (纸本)9783642043932

Recently, building sparse SVMs becomes an active research topic due to its potential applications in large scale data mining tasks. One of the most popular approaches to building sparse SVMs is to select a small subset of training samples and employ them as the support vectors. In this paper, we explain that selecting the support vectors is equivalent to selecting a number of columns from the kernel matrix, and is equivalent to selecting a subset of features in the feature selection domain. Hence, we propose to use an effective feature selection algorithm, namely the Minimum Redundancy - Maximum Relevance (MRMR) algorithm to solve the support vector selection problem. MRMR algorithm was then compared to two existing methods, namely back-fitting (BF) and pre-fitting (PF) algorithms. Preliminary results showed that MRMR generally outperformed BF algorithm while it was inferior to PF algorithm, in terms of generalization performance. However. the MRMR approach was extremely efficient and significantly faster than the two compared algorithms.

关键词： Relevance Redundancy Sparse design SVMs Machine learning

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Compressed Disjunction-Free Pattern Representation versus Essential Pattern Representation

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10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Kryszkiewicz, Marzena Warsaw Univ Technol Inst Comp Sci PL-00665 Warsaw Poland

ISBN: (纸本)9783642043932

the discovery of frequent patterns is one of the most important issues in the data mining area. A major difficulty concerning frequent patterns is huge amount of discovered patterns. the problem can be solved or at least significantly alleviated by applying concise representations of frequent patterns. A number of most concise representations use generalized disjunctive rules for reasoning about patterns. Recently, the representation based on essential patterns has been introduced, but was not confronted with the representations using generalized disjunctive rules. In this paper, we 1) prove that essential patterns with at least two elements can be defined equivalently ill terms of generalized disjunctive rules of a particular subtype and that singleton patterns are essential if their Supports do not equal 0, 2) identify the relationship between compressed disjunction-free patterns and essential ones. 3) propose new lossless representation E-CDFR of frequent patterns that is primarily based oil compressed disjunction-free patterns and uses generalized disjunctive rules to reason about other patterns. 4) prove that the new representation is never less concise than the representation based oil essential patterns.

关键词： data mining

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intelligent data engineering and automated learning - ideal 2009 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Emilio Corchado Hujun Yin

ISBN: (数字)9783642043949

ISBN: (纸本)9783642043932

the ideal conference boast a vibrant and successful history dating back to 1998, th and this edition marked the 10 anniversary, an important milestone demonstrating the increasing popularity and high quality of the ideal conferences. Burgos, the capital of medieval Spain and a lively city today, was a perfect venue to celebrate such an occasion. the conference has become a unique, established and broad int- disciplinary forum for researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine lea- ing, information processing, data mining, knowledge management, bio-informatics, neuro-informatics, bio-inspired models, agents and distributed systems, and hybrid systems. ideal 2009 received over 200 submissions. After a rigorous peer-review process, the international Programme Committee accepted 100 high-quality papers to be - cluded in the conference proceedings. In this 10th edition, a special emphasis was given on the organization of workshops and special sessions. Two workshops were organized under the framework of ideal 2009: MIR Day 2009 and Nature-Inspired Models for Industrial Applications. Five special sessions were organized by leading researchers in their fields on various topics such as Soft Computing Techniques in data Mining, - cent Advances on Swarm-Based Computing, intelligent Computational Techniques in Medical Image Processing, Advances on Ensemble learning and Information Fusion, and Financial and Business engineering (Modelling and Applications).

关键词： database Management data Mining and Knowledge Discovery Artificial Intelligence Simulation and Modeling Algorithm Analysis and Problem Complexity Computational Biology/Bioinformatics

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Exploiting spatio-temporal data for the multiobjective optimization of cellular automata models

引用

7th international conference on intelligent data engineering and automated learning (ideal 2006)

作者： Trunfio, Giuseppe A. Univ Sassari DAP I-07041 Alghero SS Italy

ISBN: (纸本)3540454853

the increased availability of remotely sensed spatio-temporal data offers the chance to improve the reliability of an important class of Cellular Automata (CA) models used for the simulation of real complex systems. To this end, this paper proposes a multiobjective approach, based on a genetic algorithm, which can present some significant advantages if compared with standard single-objective optimizations. the method exploits the available temporal sequences of spatial data in order to produce CAs which are non-dominated with respect to multiple objectives. the latter represent, in different metrics, the level of agreement between the simulated and real spatio-temporal processes. the set of non-dominated CAs proves to be a valuable source of information about potentialities and limits of a specific CA model structure.

关键词： Automata theory

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Continuous Population-Based Incremental learning with Mixture Probability Modeling for Dynamic Optimization Problems

引用

15th international conference on intelligent data engineering and automated learning (ideal)

作者： Lancucki, Adrian Chorowski, Jan Michalak, Krzysztof Filipiak, Patryk Lipinski, Piotr Univ Wroclaw Inst Comp Sci Computat Intelligence Res Grp PL-51151 Wroclaw Poland Wroclaw Univ Econ Inst Business Informat Wroclaw Poland

ISBN: (纸本)9783319108407;9783319108391

this paper proposes a multimodal extension of PBILC based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective functions more efficiently than in the unimodal case. the approach was validated on a set of synthetic benchmarks including Moving Peaks, dynamization of the Rosenbrock function and compositions of functions from the IEEE CEC'2009 competition. the results obtained in the experiments proved the efficiency of the approach in solving dynamic problems with a number of competing peaks.

关键词： evolutionary algorithms estimation of distribution algorithms dynamic optimization problems multimodal optimization

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Towards Complex Features: Competitive Receptive Fields in Unsupervised Deep Networks 19th

Towards Complex Features: Competitive Receptive Fields in Un...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Hankins, Richard Peng, Yao Yin, Hujun Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9783030034931;9783030034924

In this paper we propose a simple unsupervised approach to learning higher order features. this model is based on the recent success of lightweight approaches such as SOMNet and PCANet to the challenging task of image classification. Contrary to the more complex deep learning models such as convolutional neural networks (CNNs), these methods use naive algorithms to model the input distribution. Our endeavour focuses on the self-organizing map (SOM) based method and extends it by incorporating a competitive connection layer between filter learning stages. this simple addition encourages the second filter learning stage to learn complex combinations of first layer filters and simultaneously decreases channel depth. this approach to learning complex representations offers a competitive alternative to common deep learning models whilst maintaining an efficient framework. We test our proposed approach on the popular MNIST and challenging CIFAR-10 datasets.

关键词： Self-organising maps Unsupervised learning Deep learning Representation learning Receptive fields Pooling

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Deep Convolutional Neural Networks Based on Image data Augmentation for Visual Object Recognition 20th

Deep Convolutional Neural Networks Based on Image Data Augme...

引用

20th international conference on intelligent data engineering and automated learning (ideal)

作者： Jayech, Khaoula Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9783030336073;9783030336066

Deep Neural Networks (DNNs) have achieved a great success in machine learning. Among a lot of DNN structures, Deep Convolutional Neural Networks (DCNNs) are currently the main tool in the state-of-the-art variety of classification tasks like visual object recognition and handwriting and speech recognition. Despite wide perspectives, DCNNs have still some challenges to deal with. In previous work, we demonstrated the effectiveness of using some regularization techniques such as the dropout to enhance the performance of DCNNs. However, DCNNs need enough training data or even a class balance within datasets to conduct better results. To resolve this problem, some researchers have evoked different data augmentation approaches. this paper presents an extension of a later study. In this work, we conducted and compared the results of many experiments on CIFAR-10, STL-10 and SVHN using variant techniques of data augmentation combined with regularization techniques. the analysis results show that with the right use of data augmentation approaches, it is possible to achieve good results and outperform the state-of-the-art in this field.

关键词： Deep learning DCNN Image data augmentation Object recognition

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Mining large engineering data sets on the grid using AURA

引用

5th international conference on intelligent data engineering and automated learning (ideal 2004)

作者： Liang, B Austin, J Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)3540228810

AURA (Advanced Uncertain Reasoning Architecture) is a parallel pattern matching technology intended for high-speed approximate search and match operations on large unstructured datasets. this paper represents how the AURA technology is extended and used to search the engine data within a major UK eScience Grid project (DAME) for maintenance of Rolls-Royce aero-engines and how it may be applied in other areas. Examples of its use will be presented.

关键词： Pattern matching

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A Machine-learning Based Approach for data-Driven Identification of Heating Dynamics of Buildings' Living-Spaces 10

A Machine-Learning Based Approach for Data-Driven Identifica...

引用

10th IEEE international conference on intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Broujeny, Roozbeh Sadeghian Madani, Kurosh Chebira, Abdennasser Amarger, Veronique Hurtard, Laurent Univ Paris Est LISSI Lab EA 3956 Senart FB Inst Technol Campus Senart36-37 Rue Charpak F-77567 Lieusaint France

ISBN: (纸本)9781728140698

Modeling the heating dynamics of a given living-space of a real building remains a challenging engineering-science problem because of the quite large number of diverse kinds of involved parameters and their usually nonlinear interdependency. However, the need of such living-spaces' heating dynamics modeling appears as a foremost requirement for designing adaptive controllers scheming the complex behaviors of nowadays' smart buildings. In this context and through considering the above-mentioned complex dynamics' modeling within the slant of "Time-Series Prediction" paradigm, in this paper we propose a Machine-learning-based data-driven approach for overcoming difficulties inherent to the aforementioned challenging engineering-science problem. the proposed approach takes advantage from the nonlinear autoregressive exogenous (NARX) model's capabilities in time-series' forecasting and the Multi-Layer Perceptron's (MLP) learning and generalization skills. the proposed approach has been applied for living-space heating dynamics identification of a fully automated four-floor real building located at Senart Campus of University Paris-Est Creteil (UPEC). the obtained results assessing the accuracy of the investigated Machine-learning-based approach are reported and discussed.

关键词： Energy efficiency System identification Neural network Black box modeling component

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