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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010"

共 74 条记录，以下是21-30 订阅

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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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intelligent data engineering and automated learning – ideal 2010 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Colin Fyfe Peter Tino Darryl Charles Cesar Garcia-Osorio Hujun Yin

ISBN: (数字)9783642153815

ISBN: (纸本)9783642153808

the ideal conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. this volume contains the papers presented at the 11th international conference on intelligent data engineering and automated learning (ideal 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. the ideal conferences continue to evolve and this year’s conference was no exc- tion. the conference papers cover a wide variety of topics which can be classified by technique, aim or application. the techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. the aims include regression, classification, clustering and generic data mining. the applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

关键词： Artificial Intelligence Programming Techniques database Management Information Systems Applications (incl. Internet) Information Storage and Retrieval Computation by Abstract Devices

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Social simulation concerning making images of groups

Social simulation concerning making images of groups

引用

Joint 5th international conference on Soft Computing and intelligent Systems and 11th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2010

作者： Notsu, Akira Honda, Katsuhiro Ichihashi, Hidetomo Department of Computer Sciences and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Gakuencho 1-1 Nakaku Sakai Osaka 599-8531 Japan IEEE Japan

In this paper, we sophisticated our agent model and simulated multi-agent society. Agent is designed to compress its data by the eigenvector of its data. Additionally, in order to examine the relation between images of groups and communication network, agents' societies are investigated with several parameters. thus, this method is also designed for humanlike decisions in the learning process. the simulation results indicate our real societies, such as classes, companies and so on.

关键词： Multi agent systems

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Trajectory Based Behavior Analysis for User Verification

Trajectory Based Behavior Analysis for User Verification

引用

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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the Potential of AI in Electrical and Electronic engineering Education: A Review 11

The Potential of AI in Electrical and Electronic Engineering...

引用

11th IEEE international conference on E-learning in Industrial Electronics, ICELIE 2024

作者： Sun, Jiaqin Kwong, Chiew-Foong Buticchi, Giampaolo University of Nottingham Department of Electrical and Electronic Engineering Ningbo China

ISBN: (纸本)9798350363654

the rapid advancement of Artificial Intelligence (AI) technologies is transforming education, particularly in Electrical and Electronic engineering (EEE). this paper explores the potential applications, benefits, and challenges of Generative AI (GenAI) and Large Language Models (LLMs) in EEE education. Key areas include personalized learning, intelligent tutoring systems, automated grading, and predictive analytics. While these technologies offer significant enhancements in teaching and learning, they also present challenges such as data privacy, bias, and the need for human interaction. By examining current implementations and providing recommendations, this paper aims to guide educators and researchers in effectively integrating AI to improve EEE education. © 2024 IEEE.

关键词： Contrastive learning

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Performance comparison of FCMC and LibSVM for classification of large data sets

Performance comparison of FCMC and LibSVM for classification...

引用

Joint 5th international conference on Soft Computing and intelligent Systems and 11th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2010

作者： Ichihashi, Hidetomo Nagaura, Kazuya Notsu, Akira Honda, Katsuhiro School of Engineering Osaka Prefecture University 1-1 Gakuen-cho Nakaku Sakai Osaka 599-8531 Japan

Support Vector Machines (SVMs) deliver state-ofthe- art performance in many real-world applications such as text categorization, hand-written character recognition, image classification, biosequences analysis, etc. SVMs are one of the standard tools for machine learning and data mining. the classification performances of our proposed fuzzy c-means based classifier (FCMC) on relatively small-sized data sets have been reported. this paper reports the experimental results on large-sized data *** compare FCMC with LibSVM by Chang and Lin, which is one of the superb approaches to the SVM classifier for large-sized data sets.

关键词： Support vector machines

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Suggesting Cooking Recipes through Simulation and Bayesian Optimization 19th

Suggesting Cooking Recipes Through Simulation and Bayesian O...

引用

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

作者： Garrido-Merchan, Eduardo C. Albarca-Molina, Alejandro Univ Autonoma Madrid Francisco Tomas & Valiente 11 Madrid Spain

ISBN: (纸本)9783030034931;9783030034924

Cooking typically involves a plethora of decisions about ingredients and tools that need to be chosen in order to write a good cooking recipe. Cooking can be modelled in an optimization framework, as it involves a search space of ingredients, kitchen tools, cooking times or temperatures. If we model as an objective function the quality of the recipe, several problems arise. No analytical expression can model all the recipes, so no gradients are available. the objective function is subjective, in other words, it contains noise. Moreover, evaluations are expensive both in time and human resources. Bayesian Optimization (BO) emerges as an ideal methodology to tackle problems with these characteristics. In this paper, we propose a methodology to suggest recipe recommendations based on a Machine learning (ML) model that fits real and simulated data and BO. We provide empirical evidence with two experiments that support the adequacy of the methodology.

关键词： Bayesian optimization Cooking Simulation

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MLeNN: A First Approach to Heuristic Multilabel Undersampling

引用

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

作者： Charte, Francisco Rivera, Antonio J. del Jesus, Maria J. Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Jaen Dep Comp Sci Jaen Spain

ISBN: (纸本)9783319108407;9783319108391

learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets. In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson's Edited Nearest Neighbor Rule, is proposed. the samples to be removed are heuristically selected, instead of randomly picked. the ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.

关键词： Multilabel Classification Imbalanced learning Resampling ENN

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An Improved PBIL Algorithm for Path Planning Problem of Mobile Robots

An Improved PBIL Algorithm for Path Planning Problem of Mobi...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhang, Qingbin Cai, Manjun Zhou, Fajun Nie, Hairong Shijiazhuang Inst Railway Technol Ctr Green & Intelligent Engn Shijiazhuang 050061 Peoples R China Dalian Univ Key Lab Adv Design & Intelligent Comp Minist Educ Dalian 116622 Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9783642412783;9783642412776

the path planning problem of mobile robots is a NP-Hard problem often solved by evolutionary approaches such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). However, the algorithm's performance is often influenced heavily by the determination of the operators and the choice of related parameters. In this paper, a permutation code PBIL is proposed to solve the path planning problem. First, a free space model of the mobile robot is constructed by the MAKLINK graph;second, a sub-optimal path is generated by the Dijkstra algorithm;then global optimal path is constructed by the permutation code PBIL based on the sub-optimal path. Simulation results show that the PBIL can get satisfied solutions more simply and efficiently with fewer operators and parameters.

关键词： Path planning problem MAKLINK graph Permutation code PBIL

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Support kernel machine-based active learning to find labels and a proper kernel simultaneously

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8th international conference on intelligent data engineering and automated learning

作者： Sinohara, Yasusi Takasu, Atsuhiro Cent Res Inst Elect Power Ind 2-11-1 Iwado Kita Komae Tokyo 2018511 Japan Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (纸本)9783540772255

SVM-based active learning has been successfully applied when a large number of unlabeled samples are available but getting their labels is costly. However, the kernel used in SVM should be fixed properly before the active learning process. If the pre-selected kernel is inadequate for the target data, the learned SVM has poor performance. So, new active learning methods are required which effectively find an adequate kernel for the target data as well as the labels of unknown samples. In this paper, we propose a two-phased SKM-based active learning method and a sampling strategy for the purpose. By experiments, we show that the proposed SKM-based active learning method has quick response suited to interaction with human experts and can find an appropriate kernel among linear combinations of given multiple kernels. We also show that with the proposed sampling strategy, it converges earlier to the proper combination of kernels than with the popular sampling strategy MARGIN.

关键词： Support vector machines

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