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检索条件"机构=MOE Key Laboratory of Symbol Computation and Knowledge Engineering"

共 272 条记录，以下是21-30 订阅

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Solving job-shop scheduling problems by a novel artificial immune system

引用

18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence

作者： Ge, Hong-Wei Sun, Liang Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)3540304622

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. This paper proposes an efficient scheduling method based on artificial immune systems. In the proposed method, the initial population is generated by a proposed scheduling initialization algorithm based on the G&T algorithm, and the models of the vaccination and receptor editing are designed to improve the immune performance. The approach is tested on a set of standard instances taken from the existing standard library. The simulation results validate the effectiveness of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Intelligent networks

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DualRank: A dual-phase algorithm for optimal profit mining in retailing market

引用

10th Asian Computing Science Conference, ASIAN 2005

作者： Xu, Xiujuan Jia, Lifeng Wang, Zhe Zhou, Chunguang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)3540307672

We systematically propose a dual-phase algorithm, DualRank, to mine the optimal profit in retailing market. DualRank algorithm has two major phases which are called mining general profit phase and optimizing profit phase respectively. In the first phase, the novel sub-algorithm, ItemRank, integrates the random distribution of items into profit mining to improve the performance of item order. In the other phase, two novel optimizing sub-algorithms are proposed to ameliorating results generated in the first phase. According to the cross-selling effect and the self-profit of items, DualRank algorithm could solve the problem of item order objectively and mechanically. We conduct detailed experiments to evaluate DualRank algorithm and experiment result confirms that the new method has an excellent ability for profit mining and the performance meets the condition which requires better quality and efficiency. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Data mining

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A hidden markov model and immune particle swarm optimization-based algorithm for multiple sequence alignment

引用

18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence

作者： Ge, Hong-Wei Liang, Yan-Chun Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun 130012 China

ISBN: (纸本)3540304622

Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequences. In this paper, an immune particle swarm optimization (IPSO) is proposed, which is based on the models of the vaccination and the receptor editing in immune systems. The proposed algorithm is used to train hidden Markov models (HMMs), further, an integration algorithm based on the HMM and IPSO for the MSA is constructed. The approach is tested on a set of standard instances taken from the Benchmark Alignment database, BAliBASE. Numerical simulated results are compared with those obtained by using the Baum-Welch training algorithm. The results show that the proposed algorithm not only improves the alignment abilities, but also reduces the time cost. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Artificial intelligence

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Mining maximal sequential patterns

Mining maximal sequential patterns

引用

2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

作者： Guan, En-Zheng Chang, Xiao-Yu Wang, Zhe Zhou, Chun-Guang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)0780394224

To solve the problem that when patterns are long, frequent sequential patterns mining may generate an exponential number of results, which often makes decision-makers perplexed for there is too much useless repeated information, a novel algorithm MFSPAN (Maximal Frequent Sequential Pattern mining algorithm) to mine the complete set of maximal frequent sequential patterns in sequence databases is proposed. MFSPAN takes full advantage of the property that two different sequences may share a common prefix to reduce itemset comparing times. Experiments on standard test data show that MFSPAN is very effective. © 2005 IEEE.

关键词： Data mining

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A novel method for mining sequential patterns in datasets

A novel method for mining sequential patterns in datasets

引用

ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

作者： Xiaoyu, Chang Chunguang, Zhou Zhe, Wang Ping, Hu Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun 130012 China

ISBN: (纸本)0769525288

Sequential pattern mining is one of the most important fields in data mining. In this paper, we propose a novel algorithm FSPAN (Fast Sequential Pattern mining algorithm) to do the sequence mining. FSPAN can mine all the frequent sequential patterns in large datasets and it integrates a depth-first traversal approach with an effective pruning mechanism. This pruning mechanism solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets, which makes this method very efficient. Moreover, the databases scanned via FSPAN keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FSPAN is very effective. © 2006 IEEE.

关键词： Data mining

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Robust estimation of the fundamental matrix based on an error model

Robust estimation of the fundamental matrix based on an erro...

引用

International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zhong, Hui-Xiang Feng, Yue-Ping Pang, Yun-Jie Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)078039092X

A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique-the random sample consensus (RANSAC) to discard outliers in an initial set of matched points. It adopts the sampling strategy to generate inliers from the initial set. The second part of the method is an algorithm for computing fundamental matrix, using the output of the RANSAC. This algorithm is based on the consistent fundamental matrix estimation in a quadratic measurement error model. An extended system for determining the estimator is proposed, and an efficient implementation for solving the system-a continuation method is developed. The proposed algorithm avoids solving total eigenvalue problems. Results for both synthetic and real images show the effectiveness of the proposed method. © 2005 IEEE.

关键词： Robustness (control systems)

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Combining greedy method and genetic algorithm to identify transcription factor binding sites

Combining greedy method and genetic algorithm to identify tr...

引用

6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006

作者： Wanyu, Wang Xiaoyu, Chang Chunguang, Zhou Key Laboratory of Symbol Computation and Knowledge Engineering Juin University Ministry of Education Changchun 130012 China

ISBN: (纸本)0769526624

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA. © 2006 IEEE.

关键词： Transcription factors

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An adaptive stochastic collision detection between deformable objects using particle swarm optimization

An adaptive stochastic collision detection between deformabl...

引用

EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC

作者： Tianzhu, Wang Wenhui, Li Yi, Wang Zihou, Ge Dongfeng, Han Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)3540332375

In this paper, we present an efficient method for detecting collisions between highly deformable objects, which is a combination of newly developed stochastic method and Particle Swarm Optimization (PSO) algorithm. Firstly, our algorithm samples primitive pairs within the models to construct a discrete binary search space for PSO, and in this way user can balance performance and detection quality. Besides a particle update process is added in every time step to handle the dynamic environments caused by deformations. Our algorithm is also very general that makes no assumptions about the input models and doesn't need to store additional data structures either. In the end, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for complex deformable models in collision detection systems. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Intelligent agents

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An extended system method for consistent fundamental matrix estimation

An extended system method for consistent fundamental matrix ...

引用

International Conference on Intelligent Computing, ICIC 2005

作者： Zhong, Huixiang Feng, Yueping Pang, Yunjie Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)9783540282266

This paper is concerned with solution of the consistent fundamental matrix estimation in a quadratic measurement error model. First an extended system for determining the estimator is proposed, and an efficient implementation for solving the system-a continuation method is developed to fix on an interval in which a local minimum belongs. Then an optimization method using a quadratic interpolation is used to exactly locate the minimum. The proposed method avoids solving total eigenvalue problems. Thus the computational cost is significantly reduced. Synthetic and real images are used to verify and illustrate the effectiveness of the proposed approach. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Computer systems

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An AutoEncoder-Based Matrix Factorization Approach to Estimating Cell Proportion from Bulk Tumor RNA-seq Data

An AutoEncoder-Based Matrix Factorization Approach to Estima...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Yingze Wang, Yan Xie, Xuping Wang, Feilong Chen, Qiong Sun, Huiyan Jilin University College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9781665401265

The deconvolution of infiltrating immune cells and stromal cells from complex tumor tissues is significant for studying the impact of these cells on tumor development, as well as assisting cancer therapies. Integrating cell-specific marker genes from several references, we put forward an AutoEncoder-based matrix factorization method to estimate the cell proportion in heterogeneous samples. The proportion predicted by our method achieved over 0.95 Pearson correlation coefficient (PCC) with the ground truth. Moreover, through analyzing association between cell proportion of tumor tissues and clinical information of the tumor patients, we found that the proportion of cancer-associated fibroblast (CAF) gradually increased with the progress of tumor, and the proportion of B cell in tissues was significantly related to the five-year survival rate of tumor patients. © 2021 IEEE.

关键词： RNA

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