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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是401-410 订阅

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Bayesian approach to the concept drift in the pattern recognition problems

Bayesian approach to the concept drift in the pattern recogn...

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

作者： Turkov, Pavel Krasotkina, Olga Mottl, Vadim Tula State University 92 Lenina Ave. Tula 300600 Russia Computing Center Russian Academy of Science 40 Vavilov St. Moscow 119333 Russia

ISBN: (纸本)9783642315367

We can face with the pattern recognition problems where the influence of hidden context leads to more or less radical changes in the target concept. This paper proposes the mathematical and algorithmic framework for the concept drift in the pattern recognition problems. The probabilistic basis described in this paper is based on the Bayesian approach to the estimation of decision rule parameters. The pattern recognition procedure derived from this approach uses the general principle of the dynamic programming and has linear computational complexity in contrast to polynomial computational complexity in general kind of pattern recognition procedure. © 2012 Springer-Verlag.

关键词： Computational complexity

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Manifold-regularized minimax probability machine

Manifold-regularized minimax probability machine

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Yoshiyama, Kazuki Sakurai, Akito School of Science for Open and Environmental System Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan

ISBN: (纸本)9783642282577

In this paper we propose Manifold-Regularized Minimax Probability machine, called MRMPM. We show that Minimax Probability machine can properly be extended to semi-supervised version in the manifold regularization framework and that its kernelized version is obtained for non-linear case. Our experiments show that the proposed methods achieve results competitive to existing learning methods, such as Laplacian Support Vector machine and Laplacian Regularized Least Square for publicly available datasets from UCI machine learning repository. © 2012 Springer-Verlag.

关键词： Probability

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Unlabeled data and multiple views

Unlabeled data and multiple views

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

ISBN: (纸本)9783642282577

In many real-world applications there are usually abundant unlabeled data but the amount of labeled training examples are often limited, since labeling the data requires extensive human effort and expertise. Thus, exploiting unlabeled data to help improve the learning performance has attracted significant attention. Major techniques for this purpose include semi-supervised learning and active learning. These techniques were initially developed for data with a single view, that is, a single feature set;while recent studies showed that for multi-view data, semi-supervised learning and active learning can amazingly well. This article briefly reviews some recent advances of this thread of research. © 2012 Springer-Verlag.

关键词： Supervised learning

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Subscriber classification within telecom networks utilizing big data technologies and machine learning

Subscriber classification within telecom networks utilizing ...

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1st international workshop on Big data, streams and Heterogeneous Source mining: Algorithms, Systems, Programming Models and Applications, BigMine-12 - Held in Conjunction with SIGKDD Conference

作者： Magnusson, Jonathan Kvernvik, Tor Uppsala University Box 337 751 05 Uppsala Sweden Ericsson Research Färögatan 6 164 80 Stockholm Sweden

ISBN: (纸本)9781450315470

This paper describes a scalable solution for identifying influential subscribers in for example telecom networks. The solution estimates one weighted value of inuence out of several Social Network Analysis(SNA) metrics. The novel method for aggregation of several metrics utilizes machine learning to train models. A prototype solution has been implemented on a Hadoop platform to support scalability and to reduce hard ware cost by enabling the usage of commodity computers. The SNA algorithms have been adapted to effciently execute on the MapReduce distributed platform. The prototype solution has been tested on a Hadoop cluster. The tests have verified that the solution can scale to support networks with millions of subscribers. Both real data from a telecom network operator with 2.4 million subscribers and synthetic data for networks up to 100 million subscribers have been used to verify the scalability and accuracy of the solution. The correlation between metrics have been analyzed to identify the information gain from each metric. Copyright © 2012 ACM.

关键词： Scalability

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Semi-supervised linear discriminant analysis using moment constraints

Semi-supervised linear discriminant analysis using moment co...

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Loog, Marco Pattern Recognition Laboratory Delft University of Technology Netherlands

ISBN: (纸本)9783642282577

A semi-supervised version of Fisher's linear discriminant analysis is presented. As opposed to virtually all other approaches to semi-supervision, no assumptions on the data distribution are made, apart from the ones explicitly or implicitly present in standard supervised learning. Our approach exploits the fact that the parameters that are to be estimated in linear discriminant analysis fulfill particular relations that link label-dependent with label-independent quantities. In this way, the later type of parameters, which can be estimated based on unlabeled data, impose constraints on the former and lead to a reduction in variability of the label dependent estimates. As a result, the performance of our semi-supervised linear discriminant is expected to improve over that of its supervised equal and typically does not deteriorate with increasing numbers of unlabeled data. © 2012 Springer-Verlag.

关键词： Parameter estimation

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Applications of Discrete Geometry and Mathematical Morphology - First international workshop, WADGMM 2010, Revised Selected Papers

Applications of Discrete Geometry and Mathematical Morpholog...

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1st international workshop on Applications of Discrete Geometry and Mathematical Morphology, WADGMM 2010

ISBN: (纸本)9783642323126

The proceedings contain 11 papers. The topics discussed include: connect the dots: the reconstruction of region boundaries from contour sampling points;digital shape analysis with maximal segments;discrete curvature estimation methods for triangulated surfaces;on morphological hierarchical representations for image processing and spatial data clustering;volumetric analysis of digital objects using distance transformation: performance issues and extensions;geometric analysis of 3D electron microscopy data;machine learning as a preprocessing phase in discrete tomography;fast planarity estimation and region growing on GPU;writing reusable digital topology algorithms in a generic image processing framework;and a new image-mining technique for automation of Parkinson's disease research.

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1st international Conference on Advanced machine learning Technologies and Applications, AMLTA 2012

1st International Conference on Advanced Machine Learning Te...

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ISBN: (纸本)9783642353253

The proceedings contain 59 papers. The special focus in this conference is on Rough Sets and Applications, machine learning in pattern recognition and Image Processing, machine learning in Multimedia Computing, Bioinformatics and Cheminformatics, data Classification and Clustering, Cloud Computing and Recommender Systems, Case-Based Reasoning and data Processing, Authentication, Digital Forensics and Plagiarism Detection. The topics include: Rough sets-based machine learning over non-deterministic data;learning a table from a table with non-deterministic information;parameterised fuzzy Petri nets for approximate reasoning in decision support systems;rough sets-based rules generation approach;automatic color image segmentation based on illumination invariant and superpixelization;wavelet based statistical adapted local binary patterns for recognizing avatar faces;solving avatar captchas automatically;comparative analysis of image fusion techniques in remote sensing;density based fuzzy thresholding for image segmentation;subjectivity and sentiment analysis of Arabic;support vector machine approach for detecting events in video streams;study of feature categories for musical instrument recognition;a genetic-CBR approach for cross-document relationship identification;improved action recognition using an efficient boosting method;towards smart Egypt -the role of large scale WSNs;language for writing descriptors of outline shape of molecules;web service based approach for viral hepatitis ontology sharing and diagnosing;sampleboost for capsule endoscopy categorization and abnormality detection;advanced parallel genetic algorithm with gene matrix for global optimization;semi-possibilistic biclustering applied to discrete and continuous data;a comparative study of localization algorithms in WSNs;test cases automatic generator (TCAG);support vector machines with weighted powered kernels for data classification;an enhanced cloud-based view materialization approach for peer-to-

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Co-transfer learning via joint transition probability graph based method

Co-transfer learning via joint transition probability graph ...

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1st ACM SIGKDD workshop on Cross Domain Knowledge Discovery in Web and Social Network mining 2012, CDKD 2012

作者： Ng, Michael K. Wu, Qingyao Ye, Yunming Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong Department of Computer Science Harbin Institute of Technology Shenzhen Graduate School China

ISBN: (纸本)9781450315555

This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces;(ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively;(iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. Copyright 2012 ACM.

关键词： Iterative methods

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Online semi-supervised ensemble updates for fMRI data

Online semi-supervised ensemble updates for fMRI data

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Plumpton, Catrin O. School of Computer Science Bangor University Dean Street LL57 1UT United Kingdom

ISBN: (纸本)9783642282577

Advances in Eelectroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have opened up the possibility for real time data classification. A small amount of labelled training data is usually available, followed by a large stream of unlabelled data. Noise and possible concept drift pose a further challenge. A fixed pre-trained classifier may not always work. One solution is to update the classifier in real-time. Since true labels are not available, the classifier is updated using the predicted label, a method called naive labelling. We propose to use classifier ensembles in order to counteract the adverse effect of 'run-away' classifiers, associated with naive labelling. A new ensemble method for naive labelling is proposed. The label taken to update each member-classifier is the ensemble prediction. We use an fMRI dataset to demonstrate the advantage of the proposed method over the fixed classifier and the single classifier updated through naive labelling. © 2012 Springer-Verlag.

关键词： Magnetic resonance imaging

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On the utility of partially labeled data for classification of microarray data

On the utility of partially labeled data for classification ...

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Lausser, Ludwig Schmid, Florian Kestler, Hans A. Research Group Bioinformatics and Systems Biology Institute of Neural Information Processing University of Ulm Germany

ISBN: (纸本)9783642282577

Microarrays are standard tools for measuring thousands of gene expression levels simultaneously. They are frequently used in the classification process of tumor tissues. In this setting a collected set of samples often consists only of a few dozen data points. Common approaches for classifying such data are supervised. They exclusively use categorized data for training a classification model. Restricted to a small number of samples, these algorithms are affected by overfitting and often lack a good generalization performance. An implicit assumption of supervised methods is that only labeled training samples exist. This assumption does not always hold. In medical studies often additional unlabeled samples are available that can not be categorized for some time (i.e., "early relapse" vs. "late relapse"). Alternative classification approaches, such as semi-supervised or transductive algorithms, are able to utilize this partially labeled data. Here, we empirically investigate five semi-supervised and transductive algorithms as "early prediction tools" for incompletely labeled datasets of high dimensionality and low cardinality. Our experimental setup consists of cross-validation experiments under varying ratios of labeled to unlabeled examples. Most interestingly, the best cross-validation performance is not always achieved for completely labeled data, but rather for partially labeled datasets indicating the strong influence of label information on the classification process, even in the linearly separable case. © 2012 Springer-Verlag.

关键词： Classification (of information)

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