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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3280 条记录，以下是2471-2480 订阅

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Degradation Mode recognition for Battery of Electric Vehicle Based on ART2 Network

Degradation Mode Recognition for Battery of Electric Vehicle...

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3rd international conference on Mechanical Materials and Manufacturing Engineering (ICMMME 2013)

作者： Yu, Gang Guo, Mimi Shenzhen Grad Sch Harbin Inst Technol Sch Mech Engn & Automat Shenzhen Key Lab Digital Mfg Technol Shenzhen Peoples R China

ISBN: (纸本)9783037859223

In the process of describing the degradation process of battery of electric vehicle, degradation models under dynamic running conditions need to be analyzed in order to effectively forecast the remaining useful mileage and life of battery. This paper proposed a new method of pattern recognition based on ART2 network to distinguish the degradation mode of the battery of electric vehicle. First, the degradation mechanism is analyzed of the battery to extract the features of degradation process. Then the learning process of ART2 is designed for recognition of the degradation mode. After that, HMM is applied to research the dynamic changes of the degradation mode and predict future degradation mode sequences. Finally, experimental analysis based on battery's charge and discharge data proves the effectiveness of the proposed approach.

关键词： Battery Degradation mode ART2 pattern recognition

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Parametric Representation of Objects in Color Space Using One-Class Classifiers

Parametric Representation of Objects in Color Space Using On...

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10th international conference on machine learning and data mining (MLDM)

作者： Larin, Aleksandr Seredin, Oleg Kopylov, Andrey Kuo, Sy-Yen Huang, Shih-Chia Chen, Bo-Hao Moscow Inst Phys & Technol Moscow Russia Tula State Univ Tula Russia Natl Taiwan Univ Taipei Taiwan Natl Taipei Univ Technol Taipei Taiwan

ISBN: (纸本)9783319089799;9783319089782

Two new approaches to parametrization of specific (flame representative) part of a color space, labeled by an expert, are presented. The first concept is to apply D. Tax's one-class classifier as a steerable descriptor of such a complex volumetric structure. The second concept is based on approximation of the training data by a set of elliptic cylinders arranged along the principal components. Parameters of such elliptic cylinders describe the training set. The efficiency of the approaches has been proven by experimental study which let allowed us to compare the standard Gaussian Mixture Model based approach with the two proposed in the paper.

关键词： One-class classification Pixel classifiers Fire detection Flame detection Support Vector data Description PCA

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A Robot Waiter learning from Experiences

A Robot Waiter Learning from Experiences

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10th international conference on machine learning and data mining (MLDM)

作者： Neumann, Bernd Hotz, Lothar Rost, Pascal Lehmann, Jos Univ Hamburg Dept Informat Hamburg Germany Univ Hamburg Hamburger Informatik Technol Ctr Dept Informat Hamburg Germany

ISBN: (纸本)9783319089799;9783319089782

In this contribution, we consider learning tasks of a robot simulating a waiter in a restaurant. The robot records experiences and creates or adapts concepts represented in the web ontology language OWL 2, extended by quantitative spatial and temporal information. As a typical task, the robot is instructed to perform a specific activity in a few concrete scenarios and then expected to autonomously apply the conceptualized experiences to a new scenario. Constructing concepts from examples in a formal knowledge representation framework is well understood in principle, but several aspects important for realistic applications in robotics have remained unattended and are addressed in this paper. First, we consider conceptual representations of activity concepts combined with relevant factual knowledge about the environment. Second, the instructions can be coarse, confined to essential steps of a task, hence the robot has to autonomously determine the relevant context. Third, we propose a "Good Common Subsumer" as opposed to the formal "Least Common Subsumer" for the conceptualization of examples in order to obtain cognitively plausible results. Experiments are based on work in Project RACE(1) where a PR2 robot is employed for recording experiences, learning and applying the learnt concepts.

关键词： machine learning ontology robot activities

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ACCD: Associative Classification over Concept-Drifting data Streams

ACCD: Associative Classification over Concept-Drifting Data ...

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10th international conference on machine learning and data mining (MLDM)

作者： Waiyamai, Kitsana Kangkachit, Thanapat Saengthongloun, Bordin Rakthanmanon, Thanawin Kasetsart Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9783319089799;9783319089782

Associative classification has shown good result over many classification techniques on static datasets. However, little work has been done on associative classification over data streams. Different from data in traditional static databases, data streams typically arrive continuously and unboundedly with occasionally changing data distribution known as concept drift. In this paper, we propose a new Associative Classification over Concept-Drifting data streams, ACCD. ACCD is able to accurately detect concept drift in data streams and reduce its effect by using an ensemble of classifiers. A mechanism for statistical accuracy bounds estimation is used for supporting concept-drift detection. With this mechanism, accuracy recovering time is decreased and a situation where ensemble of classifiers drops accuracy is avoided. Compared to AC-DS (first technique on associative classification algorithm over data streams), AUEH (Accuracy updated ensemble with Hoeffding tree) and VFDT(Very Fast Decision Trees) on 4 real-world data stream datasets, ACCD exhibits the best performance in terms of accuracy.

关键词： concept drift changing environments data streams classification associative classification multiple class-sssociation rules

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Dictionary learning using EMD and Hilbert Transform for sparse modeling of environmental sounds

Dictionary learning using EMD and Hilbert Transform for spar...

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international conference on Artificial Intelligence and pattern recognition, AIPR 2014

作者： Bouchhima, Bochra Amara, Rim Alouane, Monia Turki-Hadj Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Laboratoire de Signaux et systémes Tunis1002 Tunisia Université de Carthage Institut National des Sciences Appliquées et de Technologie Tunis1080 Tunisia

This paper presents a new dictionary learning method for reconstruction tasks. Based on Empirical Mode Decomposition(EMD) and Hilbert Transformation, the dictionary is learnt, once and for all, from data having a complex structure and belonging to the same manifold. Searching sparse representation of signals over the learned dictionary is achieved by solving a minimum norm-least square error problem. Experiments on audio patterns demonstrate that it is effective. The dictionary is data adaptive and allows having signal sparse models which are well suited to restoration tasks. It has a good reconstructive sparse over-complete encoding and is largely competitive against KSVD and MOD algorithms. Our approach can be of much interest in off-line dictionary learning applications. © (2014) by Society of Digital Information and Wireless Communication (SDIWC). All rights reserved.

关键词： Empirical mode decomposition

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On selecting helpful unlabeled data for improving semi-supervised support vector machines

On selecting helpful unlabeled data for improving semi-super...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Le, Thanh-Binh Kim, Sang-Woon Department of Computer Engineering Myongji University Yongin 449-728 Korea Republic of

ISBN: (纸本)9789897580185

Recent studies have demonstrated that Semi-Supervised learning (SSL) approaches that use both labeled and unlabeled data are more effective and robust than those that use only labeled data. However, it is also well known that using unlabeled data is not always helpful in SSL algorithms. Thus, in order to select a small amount of helpful unlabeled samples, various selection criteria have been proposed in the literature. One criterion is based on the prediction by an ensemble classifier and the similarity between pairwise training samples. However, because the criterion is only concerned with the distance information among the samples, sometimes it does not work appropriately, particularly when the unlabeled samples are near the boundary. In order to address this concern, a method of training semi-supervised support vector machines (S3VMs) using selection criterion is investigated;this method is a modified version of that used in SemiBoost. In addition to the quantities of the original criterion, using the estimated conditional class probability, the confidence values of the unlabeled data are computed first. Then, some unlabeled samples that have higher confidences are selected and, together with the labeled data, used for retraining the ensemble classifier. The experimental results, obtained using artificial and real-life benchmark datasets, demonstrate that the proposed mechanism can compensate for the shortcomings of the traditional S3VMs and, compared with previous approaches, can achieve further improved results in terms of classification accuracy. Copyright © 2014 SCITEPRESS.

关键词： Support vector machines

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Adaptive Multiple-Resolution Stream Clustering

Adaptive Multiple-Resolution Stream Clustering

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10th international conference on machine learning and data mining (MLDM)

作者： Hassani, Marwan Spaus, Pascal Seidl, Thomas Rhein Westfal TH Aachen Data Management & Data Explorat Grp Aachen Germany

ISBN: (纸本)9783319089799;9783319089782

Stream data applications have become more and more prominent recently and the requirements for stream clustering algorithms have increased drastically. Due to continuously evolving nature of the stream, it is crucial that the algorithm autonomously detects clusters of arbitrary shape, with different densities, and varying number of clusters. Although available density-based stream clustering are able to detect clusters with arbitrary shapes and varying numbers, they fail to adapt their thresholds to detect clusters with different densities. In this paper we propose a stream clustering algorithm called HASTREAM, which is based on a hierarchical density-based clustering model that automatically detects clusters of different densities. The density thresholds are independently adapted to the existing data without the need of any user intervention. To reduce the high computational cost of the presented approach, techniques from the graph theory domain are utilized to devise an incremental update of the underlying model. To show the effectiveness of HASTREAM and hierarchical density-based approaches in general, several synthetic and real world data sets are evaluated using various quality measures. The results showed that the hierarchical property of the model was able to improve the quality of density-based stream clusterings and enabled HASTREAM to detect streaming clusters of different densities.

关键词： Graph theory

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Tool fault analysis with decision tree induction and sequence mining

Tool fault analysis with decision tree induction and sequenc...

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3rd international conference on Manufacturing Engineering and Process, ICMEP 2014

作者： Kerdprasop, Kittisak Kerdprasop, Nittaya Data Engineering Research Unit School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9783038350842

Tool fault analysis is a common task for process engineers in modern industries to maintain high yields of the final products. Statistical process control is a monitoring method normally adopted by most engineers. Recently, there has been enormous awareness among industrial and manufacturing engineers that intelligent techniques from the data mining and machine learning fields can be applied to discover subtle patterns from the manufacturing process data. In this paper, we present the two data mining techniques, i.e. decision tree induction and sequence mining, to discover frequently occurred patterns of the low performance wafer lots in the semiconductor manufacturing industries. The comparative analysis results of both techniques are presented through experimentation over the standard data set for the purpose of re-experimentation. © (2014) Trans Tech Publications, Switzerland.

关键词： data mining

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Detecting Programming Language from Source Code Using Bayesian learning Techniques

Detecting Programming Language from Source Code Using Bayesi...

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10th international conference on machine learning and data mining (MLDM)

作者： Khasnabish, Jyotiska Nath Sodhi, Mitali Deshmukh, Jayati Srinivasaraghavan, G. Int Inst Informat Technol Bangalore 560100 Karnataka India

ISBN: (纸本)9783319089799;9783319089782

With dozens of popular programming languages used worldwide, the number of source code files of programs available online for public use is massive. However most blogs, forums or online Q& A websites have poor searchability for specific programming language source code. Naive thumb rules based on the file extension if any are invariably used for syntax highlighting, indentation and other ways to improve readability of the code by programming language editors. A more systematic way to identify the language in which a given source file was written would be of immense value. We believe that simple Bayesiam models would be adequate for this given the intrinsic syntactic structure of any programming language. In this paper, we present Bayesian learning models for correctly identifying the programming language in which a given piece of source code was written, with high probability. We have used 20000 source code files across 10 programming languages to train and test the model using the following Bayesian classifier models - Naive Bayes, Bayesian Network and Multinomial Naive Bayes. Lastly, we show a performance comparison among the three models in terms of classification accuracy on the test data.

关键词： bayesian learning naive bayes bayesian network multinomial naive bayes programming language identifier source code detection

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Integrating local information-based link prediction algorithms with owa operator

Integrating local information-based link prediction algorith...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Liu, James N.K. He, Yu-Lin Hu, Yan-Xing Wang, Xi-Zhao Shiu, Simon C.K. Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9789897580185

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs) which have been proposed to handle this essential and crucial problem in the social network analysis. However, they can not adequately consider the so-called local information: The degrees of x and y, the number of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words, not any LILPA takes into account all the local information simultaneously. This limits the performances of LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual LILPAs. Copyright © 2014 SCITEPRESS.

关键词： Maximum entropy methods

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