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共 3357 条记录，以下是2621-2630 订阅

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BINER: BINary search based efficient regression

BINER: BINary search based efficient regression

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Bharambe, Saket Dubey, Harshit Pudi, Vikram International Institute of Information Technology - Hyderabad Hyderabad India

ISBN: (纸本)9783642315367

Regression is the study of functional dependency of one numeric variable with respect to another. In this paper, we present a novel, efficient, binary search based regression algorithm having the advantage of low computational complexity. these desirable features make BINER a very attractive alternative to existing approaches. the algorithm is interesting because instead of directly predicting the value of response variable, it recursively narrows down the range in which the response variable lies. Our empirical experiments with several real world datasets show that our algorithm, outperforms current state of art approaches and is faster by an order of magnitude. © 2012 Springer-Verlag.

关键词： Regression analysis

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data with Shifting Concept Classification Using Simulated Recurrence

Data with Shifting Concept Classification Using Simulated Re...

引用

4th international Scientific Asian conference on Intelligent Information and database Systems (ACIIDS)

作者： Sobolewski, Piotr Wozniak, Michal Wroclaw Univ Technol Dept Syst & Comp Networks Fac Elect PL-50370 Wroclaw Poland

ISBN: (纸本)9783642284878

One of the serious problems of modern pattern recognition is concept drift i.e., model changing during exploitation of a given classifier. the paper proposes how to adapt a single classifier system to the new model without the knowledge of correct classes. the proposed simulated concept recurrence is implemented in the non-recurring concept shift scenario without the drift detection mechanism. We assume that the model could change slightly, what allows us to predict a set of possible models. Quality of the proposed algorithm was estimated on the basis of computer experiment which was carried out on the benchmark dataset.

关键词： Concept shift classifier ensemble simulated recurrence machine learning

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Content independent metadata production as a machine learning problem 1

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8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Changuel, Sahar Labroche, Nicolas Université Pierre et Marie Curie - Paris 6 CNRS UMR7606 France

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Metadata provide a high-level description of digital library resources and represent the key to enable the discovery and selection of suitable resources. However the growth in size and diversity of digital collections makes manual metadata extraction an expensive task. this paper proposes a new content independent method to automatically generate metadata in order to characterize resources in a given learning objects repository. the key idea is to rely on few existing metadata to learn predictive models of metadata values. the proposed method is content independent and handles resources in different formats: text, image, video, Java applet, etc. Two classical machine learning approaches are studied in this paper: in the first approach a supervised machine learning technique classify each value of a metadata field to be predicted according to the other a-priori filled metadata fields. the second approach used the FP-Growth algorithm to discover relationships between the different metadata fields as association rules. Experiments on two well-known educational data repositories show that both approaches can enhance metadata extraction and can even fill subjective metadata fields that are difficult to extract from the content of a resource, such as the difficulty of a resource. © 2012 Springer-Verlag.

关键词： Metadata

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Clustering data stream by a sub-window approach using DCA 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Ta, Minh thuy Le thi, Hoai An Boudjeloud-Assala, Lydia UFR MIM University of Lorraine Ile du Saulcy 57045 Metz France

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

data stream is one emerging topic of data mining, it concerns many applications involving large and temporal data sets such as telephone records data, banking data, multimedia data,... For mining of such data, one crucial strategy is analysis of packet data. In this paper, we are interested in an exploratory analysis of strategies for clustering data stream based on a sub-window approach and an efficient clustering algorithm called DCA (Difference of Convex functions Algorithm). Our approach consists of separating the data on different sub-windows and then apply a DCA clustering algorithm on each sub-window. Two clustering strategies are investigated: global clustering (on the whole data set) and independent local clustering (i.e. clustering independently on each sub-window). Our aims are study: (1) the efficiency of the independent local clustering, and (2) the adequation of local clustering and global clustering based on the same DCA clustering algorithm. Comparative experiments with clustering data stream using K-Means, a standard clustering method, on different data sets are presented. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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ICIMCS 2012 - Proceedings of the 4th international conference on Internet Multimedia Computing and Service

ICIMCS 2012 - Proceedings of the 4th International Conferenc...

引用

4th international conference on Internet Multimedia Computing and Service, ICIMCS 2012

ISBN: (纸本)9781450316002

the proceedings contain 52 papers. the topics discussed include: multi-view video dissemination and delivery: challenges and solutions;binary SIFT: towards efficient feature matching verification for image search;TouchPaper ? an augmented reality application with cloud-based image recognition service;a transmission scheme for opportunistic networks with social selfish nodes;face recognition method based on dynamic threshold local binary pattern;training support vector machine through redundant data reduction;facial expression recognition using geometric and appearance features;cross community news event summary generation based on collaborative ranking;robust hand tracking by integrating appearance, location and depth cues;unsupervised human skeleton extraction from kinect depth images;real-time fingertip tracking and detection using kinect depth sensor for a new writing-in-the air system;and simulated annealing ant colony algorithm based on backfire method for QAP.

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A new learning structure heuristic of Bayesian Networks from data 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Bouhamed, Heni Masmoudi, Afif Lecroq, thierry Rebaï, Ahmed University of Rouen LITIS EA 4108 1 rue Thomas Becket 76821 Mont-Saint-Aignan Cedex France Department of Mathematics Faculty of Science of Sfax Soukra B.P 802 Sfax Tunisia Bioinformatics Unit Centre of Biotechnologie of Sfax 3018 Sfax Tunisia

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Nowadays, Bayesian Networks (BNs) have constituted one of the most complete, self-sustained and coherent formalisms useful for knowledge acquisition, representation and application through computer systems. Yet, the learning of these BNs structures from data represents a problem classified at an NP-hard range of difficulty. As such, it has turned out to be the most exciting challenge in the learning machine area. In this context, the present work's major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity problems imposed during the learning of BN-structure through a massively-huge data backlog. Our present work has been constructed according to the following framework;on a first place, we are going to proceed by defining BNs and their related problems of structure-learning from data. We, then, go on to propose a novel heuristic designed to reduce the algorithmic complexity without engendering any loss of information. Ultimately, our conceived approach will be tested on a car diagnosis as well as on a Lymphography diagnosis data-bases, while our achieved results would be discussed, along with an exposition of our conducted work's interests as a closing step to this work. © 2012 Springer-Verlag.

关键词： Bayesian networks

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Multi-label image annotation based on neighbor pair correlation chain 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Jiang, Guang Liu, Xi Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Information Technology Laboratory Fujitsu Research and Development Center Co. Ltd. Beijing 100025 China

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Image annotation plays an important role in content-based image understanding, various machine learning methods have been proposed to solve this problem. In this paper, label correlation is considered as an undirected bipartite graph, in which each label are correlated by some common hidden topics. As a result, given a label, random walk with restart on the graph supplies a most related label, repeating this precedure leads to a label chain, which keep each adjacent labels pair correlated as maximally as possible. We coordinate the labels chain with its respective classifier training on bottom feature, and guide a classifier chain to annotate an image. the experiment illustrates that our method outperform both the baseline and another popular method. © 2012 Springer-Verlag.

关键词： learning systems

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A pattern discovery model for effective text mining 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Pipanmaekaporn, Luepol Li, Yuefeng School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane QLD Australia

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

the quality of extracted features is the key issue to text mining due to the large number of terms, phrases, and noise. Most existing text mining methods are based on term-based approaches which extract terms from a training set for describing relevant information. However, the quality of the extracted terms in text documents may be not high because of lot of noise in text. For many years, some researchers make use of various phrases that have more semantics than single words to improve the relevance, but many experiments do not support the effective use of phrases since they have low frequency of occurrence, and include many redundant and noise phrases. In this paper, we propose a novel pattern discovery approach for text mining. this approach first discovers closed sequential patterns in text documents for identifying the most informative contents of the documents and then utilise the identified contents to extract useful features for text mining. We develop a novel fusion method based on Dempster-Shafer's evidential reasoning which allows to combine the pieces of document to discover the knowledge (features). To evaluate the proposed approach, we adopt the feature extraction method for information filtering (IF). the experimental results conducted on Reuters Corpus Volume 1 and TREC topics confirm that the proposed approach could achieve excellent performance. © 2012 Springer-Verlag.

关键词： Information filtering

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Nonlinear-Based Human Activity recognition Using the Kernel Technique

引用

4th international conference on Networked Digital Technologies, NDT 2012

作者： Diaf, A. Benlamri, R. Boufama, B. University of Windsor Windsor ON N9B 3P4 Canada Lakehead University Thunder Bay ON P7B 5E1 Canada

ISBN: (纸本)9783642305665

Kernel-based methods have gained great attention by researchers in the field of pattern recognition and statistical machine learning. they are the most nominated algorithms whenever a non-linear classification model is required. Human activity recognition has also been highlighted by researchers in the area of computer vision. this focusing has been triggered by the interest in many applications, such as, activity recognition in surveillance systems, robotics, wireless interfaces and interactive environments. It has been observed that the literature lacks the use of the kernel technique in the context of human activity recognition. In this context, this paper introduces a non-linear eigenvector-based recognition model that is built upon the idea of the kernel technique. the paper gives a practical study of using the kernel technique showing how much crucial choosing the right kernel function is, for the success of the linear discrimination in the feature space. the rich implementation results provided in this paper were obtained by applying the model on two of the most common used benchmark datasets in the field of human activity recognition, Kth and Weizmann. © Springer-Verlag Berlin Heidelberg 2012.

关键词： pattern recognition

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Performance Evaluation of Various K-anonymity Techniques

Performance Evaluation of Various K-anonymity Techniques

引用

4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Maheshwarkar, Nidhi Pathak, Kshitij Chourey, Vivekanand Mahakal Inst Technol Informat Technol Ujjain Madhya Pradesh India Mandsaur Inst Technol Digital Commun Mandsaur India

ISBN: (纸本)9780819490261

Today's advanced scenario where each information is available in one click, data security is the main aspect. Individual information which sometimes needs to be hiding is easily available using some tricks. Medical information, income details are needed to be kept away from adversaries and so, are stored in private tables. Some publicly released information contains zip code, sex, birth date. When this released information is linked with the private table, adversary can detect the whole confidential information of individuals or respondents, i.e. name, medical status. So to protect respondents identity, a new concept k-anonymity is used which means each released record has at least (k-1) other records in the release whose values are distinct over those fields that appear in the external data. K-anonymity can be achieved easily in case of single sensitive attributes i.e. name, salary, medical status, but it is quiet difficult when multiple sensitive attributes are present. Generalization and Suppression are used to achieve k-anonymity. this paper provides a formal introduction of k-anonymity and some techniques used with it l-diversity, t-closeness. this paper covers k-anonymity model and the comparative study of these concepts along with a new proposed concept for multiple sensitive attributes.

关键词： data privacy Anonymity l-diversity t-closeness Multiple Sensitive Attribute

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