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共 3350 条记录，以下是2611-2620 订阅

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Discriminant subspace learning based on support vectors machines 1

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

作者： Pitelis, Nikolaos Tefas, Anastasios School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Informatics Aristotle University of Thessaloniki Greece

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

A new method for dimensionality reduction and feature extraction based on Support Vector machines and minimization of the within-class data dispersion is proposed. An iterative procedure is proposed that successively applies Support Vector machines on perpendicular subspaces using the deflation transformation in such a way that the within-class variance is minimized. the proposed approach is proved to be a successive SVM using deflation kernels. the normal vectors of the successive hyperplanes contain discriminant information and they can be used as projection vectors for feature extraction and dimensionality reduction of the data. Experiments on various datasets are conducted in order to highlight the superior performance of the proposed algorithm. © 2012 Springer-Verlag.

关键词： Support vector machines

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mining ranking models from dynamic network data 1

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

作者： Macchia, Lucrezia Ceci, Michelangelo Malerba, Donato Dipartimento di Informatica Università degli Studi di Bari via Orabona 4 70126 Bari Italy

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. this phenomenon is a direct violation of the assumption that data are independently and identically distributed (i.i.d.). At the same time, it offers the unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this work, we propose a method for learning to rank from network data when data distribution may change over time. the learned models can be used to predict the ranking of nodes in the network for new time periods. the proposed method modifies the SVMRank algorithm in order to emphasize the importance of models learned in time periods during which data follow a data distribution that is similar to that observed in the new time period. We evaluate our approach on several real world problems of learning to rank from network data, coming from the area of sensor networks. © 2012 Springer-Verlag.

关键词： Sensor networks

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Class-Specific Mahalanobis Distance Metric learning for Biological Image Classification

Class-Specific Mahalanobis Distance Metric Learning for Biol...

引用

9th international conference on Image Analysis and recognition (ICIAR)

作者： Mohan, B. S. Shajee Sekhar, C. Chandra Computer Science and Engineering Department Indian Institute of Technology Madras Chennai 600036 India

ISBN: (纸本)9783642312984

Distance metric learning (DML) is an emerging field of machine learning. the basic idea behind DML is to adapt the underlying distance metric to improve the performance for the pattern analysis tasks. In this paper, we present the use of DML techniques to improve the classification accuracy of kappa-Nearest Neighbour classifier (kappa NN) used for biological image classification tasks. the distance metric learning technique is used for learning the Mahalanobis distance metric. the learning problem is cast into a Bregman optimization problem that minimizes the LogDet divergence subject to linear constraints. We propose the class-specific Mahalanobis distance metric learning for further improvement of the performance of the kappa NN classifier. Results of our studies on benchmark data sets demonstrate the effectiveness of the distance metric learning techniques in classification of biological images.

关键词： Distance metric learning kappa NN classifier Class specific Mahalanobis distancemetric learning Bregman divergence LogDet divergence

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BINER: BINary search based efficient regression

BINER: BINary search based efficient regression

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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...

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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

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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

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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

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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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