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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41-50 订阅

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Generalized Derivative Based Kernelized learning Vector Quantization

Generalized Derivative Based Kernelized Learning Vector Quan...

引用

11th international conference on intelligent data engineering and automated learning

作者： Schleif, Frank-Michael Villmann, thomas Hammer, Barbara Schneider, Petra Biehl, Michael Univ Bielefeld Dept Techn Univ Str 21-23 D-33615 Bielefeld Germany Univ Appl Faculty Math Natural & CS D-09648 Mittweida Germany Univ Groningen Johann Bernoulli Inst Math & CS NL-9700 Groningen Netherlands

ISBN: (纸本)9783642153808

We derive a novel derivative based version of kernelized Generalized learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning.

关键词： Vector quantization

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Visualising and clustering video data

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8th international conference on intelligent data engineering and automated learning

作者： Fyfe, Colin Ooi, Wei Chuang Ko, Hanseok Univ Paisley Appl Computat Intelligence Res Unit Paisley PA1 2BE Renfrew Scotland Korea Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9783540772255

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [1]. But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts [6]. We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels and show that the new mapping achieves better results than the standard Self-Organizing Map.

关键词： data visualization

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On building XML data warehouses

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

作者： Rusu, Laura Irina Rahayu, Wenny Taniar, David LaTrobe University Department of Computer Science and Computer Engineering BundooraVIC3086 Australia Monash University School of Business Systems ClaytonVIC3800 Australia

ISBN: (纸本)3540228810

Developing a data warehouse for XML documents involves two major processes: one of creating it, by processing XML raw documents into a specified data warehouse repository;and the other of querying it, by applying techniques to better answer user’s queries. the proposed methodology in our paper on building XML data warehouses covers processes such as data cleaning and integration, summarization, intermediate XML documents, and updating/ linking existing documents and creating fact tables. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data warehouses

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Combining Gaussian mixture models

引用

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

作者： Lee, Hyoung-Joo Cho, Sungzoon Department of Industrial Engineering Seoul National University San 56-1 Shillim-dong Kwanak-gu Seoul151-744 Korea Republic of

ISBN: (纸本)3540228810

A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. this paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters. Simulations on synthetic datasets demonstrate that a committee of as few as 10 models outperforms a single model. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Probability density function

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A Genetic Algorithm-based AutoML Approach for Large-scale Traffic Speed Prediction 5

A Genetic Algorithm-based AutoML Approach for Large-scale Tr...

引用

IEEE 5th international conference on intelligent Transportation engineering (ICITE)

作者： You, Junwei Northeast Normal Univ Rutgers Univ Newark Inst NENU Jinzhong Peoples R China

ISBN: (纸本)9781728194097

With the continuous innovation of computer science as well as the big data acquisition technology, machine learning (ML), developed as a state-of-art framework, now has been comprehensively applied in speed prediction tasks. However, ML methods usually require intensive hyper-parameter tuning, which hinders the practical deployment of ML models. In view of this, this paper proposes an automated machine learning (AutoML) framework for speed prediction, which enables the prediction work to be accomplished in a much more timesaving and convenient way as well as in high prediction accuracy. the proposed framework utilizes the Genetic Algorithm (GA) following its four major procedures: Genome coding, Crossover, Mutation and Selection to automatically search for the optimal neural network architectures and hyperparameters. the proposed framework is examined on a real-world large-scale dataset in the city of Berlin, Germany. the experimental results demonstrate that the proposed method outperforms other benchmarking methods by a significant margin. Sensitivity analysis is also conducted to show the robustness of the proposed method. this study demonstrates the great penitential of using AutoML in traffic speed prediction and other related transportation applications.

关键词： AutoML deep learning traffic speed prediction intelligent Transportation System (ITS) machine learning

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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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Art and Automation of Teaching Malware Reverse engineering 5th

Art and Automation of Teaching Malware Reverse Engineering

引用

5th international conference on learning and Collaboration Technologies (LCT) Held as Part of 20th international conference on Human-Computer Interaction (HCI international)

作者： Lepik, Toomas Maennel, Kaie Ernits, Margus Maennel, Olaf Tallinn Univ Technol Tallinn Estonia

ISBN: (纸本)9783319911526;9783319911519

the threat environment is rapidly changing and the cyber security skill shortage is a widely acknowledged problem. However, teaching such skills and keeping professionals up-to-date is not trivial. New malware types appear daily, and it requires significant time and effort by a teacher to prepare a unique, current and challenging courses in the malware reverse engineering. Novel teaching methods and tools are required. this paper describes an experience with an automated hands-on learning environment in a malware reverse engineering class taught at Tallinn University of Technology in Estonia. Our hands-on practical lab is using a fully automated Cyber Defense Competition platform intelligent Training Exercise Environment (i-tee) [1] combined with typical Capture-the-Flag competition structure and open-source tools where possible. We describe the process of generating a unique and comparable reverse-engineering challenge and measuring the students' progress through the process of analysis, reporting flags and debugging data, recording and taking into account their unique approach to the task. We aim to measure the students' using the Bloom's taxonomy, i.e., mastering the art of malware reverse engineering at the higher cognitive levels. the presented teaching and assessment method builds foundation for enhancing the future malware reverse engineering training quality and impact.

关键词： Higher education teaching Cyber defence exercises Malware reverse engineering

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Deep Reinforcement learning based Multi-Objective Systems for Financial Trading 5

Deep Reinforcement Learning based Multi-Objective Systems fo...

引用

5th IEEE international conference on Recent Advances and Innovations in engineering (IEEE - ICRAIE)

作者： Bisht, Kiran Kumar, Arun GB Pant Univ Agr & Technol Dept Math Stat & Comp Sci Pantnagar 263145 Uttarakhand India

ISBN: (纸本)9781728188676

Because of the risky nature of stock market, most people do not feel a secure option to invest their money in financial trading. Focusing on this basic concern of investors much research efforts are devoted to develop automated trading systems that make intelligent decisions according to the market situation and help investor to make profit beside risk. In contrast, in this paper we proposed multi-objective systems based on deep reinforcement learning for stock trading. Target of the multi-objective systems is to get maximum profit by adjusting risk. We design the whole structure of systems consisting two deep neural networks first is LSTM autoencoder for robust feature extraction and second deep reinforcement learning with LSTM recurrent neural network for decision making in order to achieve the investor's goal. We conduct an experiment on real historic data for verification of the systems and compare them with conventional trading systems.

关键词： Reinforcement learning (RL) Deep learning (DL) Long Short Term Memory (LSTM) Financial trading

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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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K nearest sequence method and its application to churn prediction

引用

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

作者： Ruta, Dymitr Nauck, Detlef Azvine, Ben BT Grp Chief Technol Off Ipswich IP5 3RE Suffolk England

ISBN: (纸本)3540454853

In telecom industry high installation and marketing costs make it between six to ten times more expensive to acquire a new customer than it is to retain the existing one. Prediction and prevention of customer chum is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is lots of related temporal data sequences generated as a result of customer interaction with the service provider. Existing churn prediction methods like decision tree typically just classify customers into chumers or non-chumers while completely ignoring the timing of chum event. Given histories of other customers and the current customer's data, the presented model proposes a new k nearest sequence (kNS) algorithm along with temporal sequence fusion technique to predict the whole remaining customer data sequence path up to the chum event. It is experimentally demonstrated that the new model better exploits time-ordered customer data sequences and surpasses the existing churn prediction methods in terms of performance and offered capabilities.

关键词： Industrial engineering

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