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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是91-100 订阅

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Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification

Interval Filter: A Locality-Aware Alternative to Bloom Filte...

引用

11th international conference on intelligent data engineering and automated learning

作者： Quislant, Ricardo Gutierrez, Eladio Plata, Oscar Zapata, Emilio L. Univ Malaga Dept Comp Architecture ETSI Informat E-29071 Malaga Spain

ISBN: (纸本)9783642153808

Bloom filters are data structures that can efficiently represent a set of elements providing operations of insertion and membership testing. Nevertheless, these filters may yield false positive results when testing for elements that have not been previously inserted. In general, higher false positive rates are expected for sets with larger cardinality with constant filter size. this paper shows that for sets where a distance metric can be defined, reducing the false positive rate is possible if elements to be inserted exhibit locality according to this metric. In this way, a hardware alternative to Bloom filters able to extract spatial locality features is proposed and analyzed.

关键词： data structures

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Neural data Analysis and Reduction Using Improved Framework of Information-Preserving EMD

Neural Data Analysis and Reduction Using Improved Framework ...

引用

11th international conference on intelligent data engineering and automated learning

作者： Mehboob, Zareen Yin, Hujun Univ Manchester Manchester M13 9PL Lancs England

ISBN: (纸本)9783642153808

this paper presents several improvements to the framework of information-preserving empirical mode decomposition (EMD). the basic framework was presented in our previous work [1]. the method decomposes a non-stationary neural response into a number of oscillatory modes varying in information content. After the spectral information analysis only few modes, taking part in stimulus coding, are retrieved for further analysis. the improvements and enhancement have been proposed for the steps involved in information quantification and modes extraction. An investigation has also been carried out for compression of retrieved informative modes of the neural signal in order to achieve a lower bit rate using the proposed framework. Experimental results are presented.

关键词： Empirical mode decomposition

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Selection of Structures with Grid Optimization, in Multiagent data Warehouse

Selection of Structures with Grid Optimization, in Multiagen...

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11th international conference on intelligent data engineering and automated learning

作者： Gorawski, Marcin Bankowski, Slawomir Gorawski, Michal Silesian Tech Univ Gliwice Poland

ISBN: (纸本)9783642153808

the query optimization problem in data base and data warehouse management systems is quite similar. Changes to Joins sequences, projections and selections, usage of indexes, and aggregations are all decided during the analysis of an execution schedule. the main goal of these changes is to decrease the query response time. the optimization operation is often dedicated to a single node. this paper proposes optimization to grid or cluster data warehouses / databases. Tests were conducted in a multi-agent environment, and the optimization focused not only on a single node but on the whole system as well. A new idea is proposed here with multi-criteria optimization that is based on user-given parameters. Depending on query time, result admissible errors, and the level of system usage, task results were obtained along with grid optimization.

关键词： GRID data warehouse DWS MGO OLAP MAS approximate results

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Clustering and Visualizing SOM Results

Clustering and Visualizing SOM Results

引用

11th international conference on intelligent data engineering and automated learning

作者： Costa, Jose Alfredo F. Fed Univ Dept Elect Engn Natal RN Brazil

ISBN: (纸本)9783642153808

Self-organizing maps (SOM) had been used for input data quantization and visual display of data, an important property that does not exist in most of clustering algorithms. Effective data clustering using SOM involves two or three steps procedure. After proper network training, units can be clustered generating regions of neurons which are related to data clusters. the basic assumption relies on the data density approximation by the neurons through unsupervised learning. this paper presents a gradient-based SOM visualization method and compares it with U-matrix. It also discusses steps toward clustering using SOM and morphological operators. Results using benchmark datasets show that the new method is more robust to choice of parameters in the filtering phase than the conventional method. the paper also proposes an enhancing method to map visualization taking advantage of the neurons activity, which improve cluster detection especially in small maps.

关键词： data clustering visualization self-organizing maps

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the Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces

The Penalty Avoiding Rational Policy Making Algorithm in Con...

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11th international conference on intelligent data engineering and automated learning

作者： Miyazaki, Kazuteru Natl Inst Acad Degrees & Univ Evaluat Kodaira Tokyo 1878587 Japan

ISBN: (纸本)9783642153808

Reinforcement learning involves learning to adapt to environments through the presentation of rewards - special input - serving as clues. To obtain quick rational policies, profit sharing, rational policy making algorithm, penalty avoiding rational policy making algorithm (PARP), PS-r* and PS-r# are used. they are called Exploitation-oriented learning (XoL). When applying reinforcement learning to actual problems, treatment of continuous-valued input and output are sometimes required. A method based on PARP is proposed as a XoL method corresponding to the continuous-valued input, but continuous-valued output cannot be treated. We study the treatment of continuous-valued output suitable for a XoL method in which the environment includes both a reward and a penalty. We extend PARP in the continuous-valued input to continuous-valued output. We apply our proposal to the pole-cart balancing problem and confirm its validity.

关键词： Reinforcement learning

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Extracting Features from an Electrical Signal of a Non-Intrusive Load Monitoring System

Extracting Features from an Electrical Signal of a Non-Intru...

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11th international conference on intelligent data engineering and automated learning

作者： Figueiredo, Marisa B. de Almeida, Ana Ribeiro, Bernardete Martins, Antonio Univ Coimbra Ctr Informat & Syst Polo 2 P-3030290 Coimbra Portugal ISA P-3030320 Coimbra Portugal

ISBN: (纸本)9783642153808

Improving energy efficiency by monitoring household electrical consumption is of significant importance with the present-day climate change concerns. A solution for the electrical consumption management problem is the use of a non-intrusive load monitoring system (NILM). this system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched on appliances. An effective device identification (ID) requires a signature to be assigned for each appliance. Moreover, to specify an ID for each device, signal processing techniques are needed for extracting the relevant features. this paper describes a technique for the steady-states recognition in an electrical digital signal as the first stage for the implementation of an innovative NILM. Furthermore, the final goal is to develop an intelligent system for the identification of the appliances by automated learning. the proposed approach is based on the ratio value between rectangular areas defined by the signal samples. the computational experiments show the method effectiveness for the accurate steady-states identification in the electrical input signals.

关键词： automated learning and identification feature extraction and classification non-intrusive load monitoring

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the M-OLAP Cube Selection Problem: A Hyper-polymorphic Algorithm Approach

The M-OLAP Cube Selection Problem: A Hyper-polymorphic Algor...

引用

11th international conference on intelligent data engineering and automated learning

作者： Loureiro, Jorge Belo, Orlando Escola Super Tecnol & Gestao Inst Politecn Viseu Dept Informat Campus Politecn Repeses P-3505510 Viseu Portugal Univ Minho Escola Engn Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783642153808

OLAP systems depend heavily on the materialization of multidimensional structures to speed-up queries, whose appropriate selection constitutes the cube selection problem. However, the recently proposed distribution of OLAP structures emerges to answer new globalization's requirements, capturing the known advantages of distributed databases. But this hardens the search for solutions, especially due to the inherent heterogeneity, imposing an extra characteristic of the algorithm that must be used: adaptability. Here the emerging concept known as hyper-heuristic can be a solution. In fact, having an algorithm where several (meta-)heuristics may be selected under the control of a heuristic has an intrinsic adaptive behavior. this paper presents a hyper-heuristic polymorphic algorithm used to solve the extended cube selection and allocation problem generated in M-OLAP architectures.

关键词： Hyper-Heuristics Distributed data Cube Selection Multi-Node OLAP Systems Optimization

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Trajectory Based Behavior Analysis for User Verification

Trajectory Based Behavior Analysis for User Verification

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11th international conference on intelligent data engineering and automated learning

作者： Pao, Hsing-Kuo Lin, Hong-Yi Chen, Kuan-Ta Fadlil, Junaidillah Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Taipei 115 Taiwan

ISBN: (纸本)9783642153808

Many of our activities on computer need a verification step for authorized access. the goal of verification is to tell apart the true account owner from intruders. We propose a general approach for user verification based on user trajectory inputs. the approach is labor-free for users and is likely to avoid the possible copy or simulation from other non-authorized users or even automatic programs like bots. Our study focuses on finding the hidden patterns embedded in the trajectories produced by account users. We employ a Markov chain model with Gaussian distribution in its transitions to describe the behavior in the trajectory. To distinguish between two trajectories, we propose a novel dissimilarity measure combined with a manifold learnt tuning for catching the pairwise relationship. Based on the pairwise relationship, we plug-in any effective classification or clustering methods for the detection of unauthorized access. the method can also be applied for the task of recognition, predicting the trajectory type without pre-defined identity. Given a trajectory input, the results show that the proposed method can accurately verify the user identity, or suggest whom owns the trajectory if the input identity is not provided.

关键词： Verification Behavior analysis Account security Trajectory Dissimilarity measure Manifold learning Isomap

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Privacy preserving technique for euclidean distance based mining algorithms using a wavelet related transform

Privacy preserving technique for euclidean distance based mi...

引用

11th international conference on intelligent data engineering and automated learning, ideal 2010

作者： Kadampur, Mohammad Ali D.v.l.n., Somayajulu Warangal A.P. 506004 India

ISBN: (纸本)3642153801

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： data mining

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learning negotiation policies using IB3 and bayesian networks

Learning negotiation policies using IB3 and bayesian network...

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11th international conference on intelligent data engineering and automated learning, ideal 2010

作者： Nalepa, Gislaine M. Ávila, Bráulio C. Enembreck, Fabrício Scalabrin, Edson E. Graduate Program on Applied Computer Science PUCPR Pontifical Catholic University of Paraná R. Imaculada Conceição Curitiba PR 1155 Brazil

ISBN: (纸本)3642153801

this paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Bayesian networks

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