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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1778 条记录，以下是1571-1580 订阅

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Multi-agent system approach to react to sudden environmental changes 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Raudys, Sarunas Mitasiunas, Antanas Inst Math & Informat Akad 4 LT-08663 Vilnius Lithuania Vilnius Univ Vilnius Lithuania

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Many processes experience abrupt changes in their dynamics. this causes problems for some prediction algorithms which assume that the dynamics of the sequence to be predicted are constant, or at least only change slowly over time. In this paper the problem of predicting sequences with sudden changes in dynamics is considered. For a model of multivariate Gaussian data we derive expected generalization error of standard linear Fisher classifier in situation where after unexpected task change, the classification algorithm learns on a mixture of old and new data. We show both analytically and by an experiment that optimal length of learning sequence depends on complexity of the task, input dimensionality, on the power and periodicity of. the changes. the proposed solution is to consider a collection of agents, in this case non-linear single layer perceptrons (agents), trained by a memetic like learning algorithm. T e most successful agents are voting for predictions. A grouped structure of the agent population assists in obtaining favorable diversity in the agent population. Efficiency of socially organized evolving multi-agent system is demonstrated on an artificial problem.

关键词： generalization dimensionality evolution learning multi-agent systems neural networks sample size

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Error protection method for embedded wavelet codec

Error protection method for embedded wavelet codec

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Ding, Xue-Wen Yang, Zhao-Xuan Wang, Zeng-Min Zheng, Hong-Xing Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Technol & Educ Sch Elect Informat Engn Tianjin 300222 Peoples R China

ISBN: (纸本)9781424410651

this paper presents an error protection method for embedded wavelet codec by combining data hiding with error concealment. For the lowest frequency coefficients, a prolection scheine based data hiding is peifol-ined. the lowest frequency coefficients, which are taken as the hidden data, are extracted from the compressed bilstream, and embedded back into the same bitstream. the restored hidden data is used to conceal errors. the coefficients that cannot be recovered in other high frequency coefficients reconstruction are predicted through linear interpolation based oil inter-subband correlation. Experimental results show that the proposed method achieves good and stable error performance with minimal additional redundancy.

关键词： sPIHT coding error protection data hiding error concealment

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A new method for constructing clustering ensembles

A new method for constructing clustering ensembles

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Luo, Hui-Lan Xie, Xiao-Bing Li, Kang-Shun Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China

ISBN: (纸本)9781424410651

there are some general procedures to generate the clusterings in clustering ensembles. One can apply different algorithms to create different clusterings of the data. Some clustering algorithms like k-means require initialization Of parameters. Different initializations can lead to different clustering results. the parameters of a clustering algorithm, such as the number of clusters, can be altered to create different data clusterings. Different versions of the data can also be used as the input to the clustering algorithm, leading to different partitions. In this paper, we propose a new scheme for constructing multiple independent clusterings using additional artificially generated data. then, we compare our new method with seven general clustering ensemble constructing methods. the experiments show that our approach can achieve higher or comparable performance than the other methods.

关键词： clustering ensemble artificial data resampling

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CCIC: Consistent common itemsets classifier 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Shidara, Yohji Nakamura, Atsuyoshi Kudo, Mineichi Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku Kita 14Nishi 9 Sapporo Hokkaido 0600814 Japan

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

We propose a novel approach which extracts consistent (100% confident) rules and builds a classifier with them. Recently, associative classifiers which utilize association rules have been widely studied. Indeed, the associative classifiers often outperform the traditional classifiers. In this case, it is important to collect high quality (association) rules. Many algorithms find only high support rules, because decreasing the minimum support to be satisfied is computationally demanding. However, it may be effective to collect low support but high confidence rules. therefore, we propose an algorithm that produces a wide variety of 100% confident rules including low support rules. To achieve this goal, we adopt a specific-to-general rule searching strategy, in contrast to the previous many approaches. Our experimental results show that the proposed method achieves higher accuracies in several datasets taken from UCI machine learning repository.

关键词： data structures

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data selection using SASH trees for support vector machines 1

引用

5th international conference on machine learning and data Mining in pattern recognition

作者： Sun, Chaofan Vilalta, Ricardo Univ Houston Dept Comp Sci 4800 Calhoun Rd Houston TX 77204 USA Ctr Res & Adv Studies CINVESTAV Guadalajara 45010 Jalisco Mexico

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper presents a data preprocessing procedure to select support vector (SV) candidates. We select decision boundary region vectors (BRVs) as SV candidates. Without the need to use the decision boundary, BRVs can be selected based on a vector's nearest neighbor of opposite class (NNO). To speed up the process, two spatial approximation sample hierarchical (SASH) trees are used for estimating the BRVs. Empirical results show that our data selection procedure can reduce a full dataset to the number of SVs or only slightly higher. Training with the selected subset gives performance comparable to that of the full dataset. For large datasets, overall time spent in selecting and training on the smaller dataset is significantly lower than the time used in training on the full dataset.

关键词： sampling methods support vector machines

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Fast-maneuvering target seeking based on double-action Q-learning 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Ngai, Daniel C. K. Yung, Nelson H. C. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper, a reinforcement learning method called DAQL is proposed to solve the problem of seeking and homing onto a fast maneuvering target, within the context of mobile robots. this Q-learning based method considers both target and obstacle actions when determining its own action decisions, which enables the agent to learn more effectively in a dynamically changing environment. It particularly suits fast-maneuvering target cases, in which maneuvers of the target are unknown a priori. Simulation result depicts that the proposed method is able to choose a less convoluted path to reach the target when compared to the ideal proportional navigation (IPN) method in handling fast maneuvering and randomly moving target. Furthermore, it can learn to adapt to the physical limitation of the system and do not require specific initial conditions to be satisfied for successful navigation towards the moving target.

关键词： moving object navigation reinforcement learning Q-learning

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A kind of trading off algorithm of object classification based on intelligent data fusion

A kind of trading off algorithm of object classification bas...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Pan, Xiu-Qin Lu, Yong Zhao, Yue Cao, Yong-Cun Sun, Yuan Cent Univ Sch Math & Comp Sci Beijing 100081 Peoples R China

ISBN: (纸本)9781424410651

A kind of trading off algorithm off target classification combined with double mode intelligent fusion is presented, Applying neuron-fuzzy technique to the synthesis of the complementary information between radar and infrared, through combining neuron-fuzzy, technique with D-S evidence fusion theory, the ability, of target classification could be improved At the same time, according to the effective defection range of sensors, reasonably select the target features that can ensure the accurate target classification so that the computation load of the algorithm can be largely decreased compared to the infrared single model fusion. Simulalion results illustrate that the trading off algorithm is effective.

关键词： fusion classification fuzzy neural network

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PE-PUC: A graph based PU-learning approach for text classification 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Yu, Shuang Li, Chunping Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

this paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed with positive and negative documents. this problem is also called PU-learning. the key feature of PU-learning is that there is no negative document for training. Recently, several approaches have been proposed for solving this problem. Most of them are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. Generally speaking, these existing approaches do not perform well when the size of P is small. In this paper, we propose a new approach aiming at improving the system when the size of P is small. this approach combines the graph-based semi-supervised learning method with the two-step method. Experiments indicate that our proposed method performs well especially when the size of P is small.

关键词： learning systems

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Comparison of a novel combined ECOC strategy with different multiclass algorithms together with parameter optimization methods 1

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5th international conference on machine learning and data Mining in pattern recognition

作者： Hillsmann, Marco Friedrich, Christoph M. Univ Cologne D-5000 Cologne 41 Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 Birlinghoven Germany

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper we consider multiclass learning tasks based on Support Vector machines (SVMs). In this regard, currently used methods are One-Against-All or One-Against-One, but there is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called Comb-ECOC, which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A problem with the usage of a multiclass method is the proper choice of parameters. Many users only take the default parameters of the respective learning algorithms (e.g. the regularization parameter C and the kernel parameter gamma). We tested different parameter optimization methods on different learning algorithms and confirmed the better performance of One-Against-One versus One-Against-All, which can be explained by the maximum margin approach of SVMs.

关键词： learning algorithms

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Study of myocardial infraction feature extraction using subjective introduction based method

Study of myocardial infraction feature extraction using subj...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Ge, Ding-Fei Sun, Li-Hui Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310012 Peoples R China

ISBN: (纸本)9781424410651

Most of the existing studies on myocardial infraction (MI) feature extraction are based on certain important components. the subjective significance based method was introduced into the feature extraction from an entire ECG segment for the classification in the paper. the method was employed to discriminate the assumed prior class from the other classes and separate each of the classes at the same time. the data in the analysis including healthy control (HC), myocardial infraction in early stage and acute myocardial infraction (AMI) was collected from PTB diagnostic ECG database which is the latest public database for various research purposes. the results show that the proposed method can obtain the effective features from the ECGs with 12-leads for the classification purpose.

关键词： myocardial infraction features classification subjective significance hyperdimensional data

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