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检索条件"主题词=expectation-maximisation algorithm"

共 495 条记录，以下是471-480 订阅

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Variational Inference for Crowdsourcing 12

Variational Inference for Crowdsourcing

引用

Annual Conference on Neural Information Processing Systems

作者： Qiang Liu Jian Peng Alexander Ihler ICS UC Irvine TTI-C & CSAIL MIT

ISBN: (纸本)9781627480031

Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the computational task of aggregating the crowdsourced labels provided by a collection of unreliable annotators. We approach this problem by transforming it into a standard inference problem in graphical models, and applying approximate variational methods, including belief propagation (BP) and mean field (MF). We show that our BP algorithm generalizes both majority voting and a recent algorithm by Karger et al. [1], while our MF method is closely related to a commonly used EM algorithm. In both cases, we find that the performance of the algorithms critically depends on the choice of a prior distribution on the workers' reliability; by choosing the prior properly, both BP and MF (and EM) perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.

关键词： Variational Inference Crowdsource Prior distribution algorithms expectation-maximisation algorithm Dataset Belief propagation Graphical models Calculus of variation

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Incremental Gaussian Processes 02

Incremental Gaussian Processes

引用

Annual Conference on Neural Information Processing Systems

作者： Joaquin Quinonero-Candela Ole Winther Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Lyngby Denmark

ISBN: (纸本)0262025507

In this paper, we consider Tipping's relevance vector machine (RVM) and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call Subspace EM (SSEM). Working with a subset of active basis functions, the sparsity of the RVM solution will ensure that the number of basis functions and thereby the computational complexity is kept low. We also introduce a mean field approach to the intractable classification model that is expected to give a very good approximation to exact Bayesian inference and contains the Laplace approximation as a special case. We test the algorithms on two large data sets with O(10~3 ― 10~4) examples. The results indicate that Bayesian learning of large data sets, e.g. the MNIST database is realistic.

关键词： Gaussian processes basis functions Bayesian learning Bayesian inference Representation of data Dataset Incremental Incremental Training vector computer approximation expectation-maximisation algorithm Subset

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A NEW BAYESIAN APPROACH TO TEXTURED IMAGE SEGMENTATION: TURBO SEGMENTATION

A NEW BAYESIAN APPROACH TO TEXTURED IMAGE SEGMENTATION: TURB...

引用

European signal processing conference

作者： Frederic Lehmann INSTITUT TELECOM TELECOM SudParis departement CITI UMR-CNRS 5157 9 rue Charles Fourier 91011 Evry France

ISBN: (纸本)9781617388767

We consider the problem of semi-supervised segmentation of textured images. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A new segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes, is obtained based on a factor graph approach. The proposed method estimates the unknown parameters using the expectation-Maximization algorithm.

关键词： Bayes methods expectation-maximisation algorithm Factor Graph Unknown parameter

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An EM-based estimation for a two-level traitor tracing scheme

An EM-based estimation for a two-level traitor tracing schem...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Faten Chaabane Maha Charfeddine William Puech Chokri Ben Amar REsearch Groups in Intelligent Machines ENIS Sfax 3038 Tunisia LIRMM Laboratory UMR 5506 CNRS University of Montpellier161 Rue Ada 34392 Montpellier Cedex 05 France

ISBN: (纸本)9781509018987

In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The expectation Maximization algorithm has shown to be an efficient solution to estimate the collusion channel and thus to tune the Tardos accusation functions. In this paper, we explore the impact of this algorithm in a group-based tracing scheme to deal with the computational costs and the invariance of the Tardos accusation performance. The tracing scheme we propose benefits from a twofold accusation process. Indeed, in a first time, it is based on a two-level tracing strategy which consists in tracing guilty groups in a first level with the Boneh Shaw tracing code and in retrieving at least one colluder in accused groups with Tardos code in the second level. This strategy has reduced efficiently the decoding process of the Tardos code. The main shift we propose in the second level is to apply the expectation Maximization algorithm to be tightly tied to collusion yielded by colluders and hence to find the more accurate Tardos accusation functions. The performance of the resulting tracing scheme is evaluated according to different criteria and promising results have been achieved when compared to the existing tracing schemes proposed in the literature.

关键词： main challenges colluders' strategy expectation Maximization RAPPING expectation-maximisation algorithm Scheme

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A Probabilistic Framework for Deep Learning 16

A Probabilistic Framework for Deep Learning

引用

Annual Conference on Neural Information Processing Systems

作者： Ankit B. Patel Tan Nguyen Richard G. Baraniuk Baylor College of Medicine Rice University Rice University

ISBN: (纸本)9781510838819

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our framework provides new insights into the successes and shortcomings of DCNs as well as a principled route to their improvement. DRMM training via the expectation-Maximization (EM) algorithm is a powerful alternative to DCN back-propagation, and initial training results are promising. Classification based on the DRMM and other variants outperforms DCNs in supervised digit classification, training 2-3 x faster while achieving similar accuracy. Moreover, the DRMM is applicable to semi-supervised and unsupervised learning tasks, achieving results that are state-of-the-art in several categories on the MNIST benchmark and comparable to state of the art on the CIFAR10 benchmark.

关键词： Frameworks Probability state of the art BENCHMARKS Machine Learning initial training Arts Unsupervised learning expectation-maximisation algorithm

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UNSUPERVISED CHANGE DETECTION WITH HIGH-RESOLUTION SAR IMAGES BY EDGE-PRESERVING MARKOV RANDOM FIELDS AND GRAPH-CUTS

UNSUPERVISED CHANGE DETECTION WITH HIGH-RESOLUTION SAR IMAGE...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Gabriele Moser Sebastiano B. Serpico University of Genoa Dept. of Naval Electrical Electronic and Telecommunications Eng. (DITEN)

ISBN: (纸本)9781467311601

Change detection techniques represent important tools for environmental monitoring and damage assessment after environmental disasters. However, change detection methods that were found accurate for coarser-resolution SAR are often ineffective with current very high resolution (VHR) satellite SAR due to the need to suitably model the contextual and geometrical information associated with VHR data. In this paper, a novel unsupervised change detection technique is proposed for VHR SAR based on Markov random fields (MRFs), line processes, and a dictionary of SAR-specific probability density models. The estimation of the parameters of the proposed MRF model is carried out through the expectation-maximization algorithm and the method of log-cumulants. Graph cuts are used to minimize the energy function of the MRF model because of their capability to approach global minima or strong local minima in acceptable computation times. The proposed method is experimented with COSMO-SkyMed images acquired before and after an earthquake.

关键词： Unsupervised change detection Markov random fields Line processes Graph cuts expectation-maximization Method of log-cumulants Synthetic aperture radar Specific absorption rate Rescue SAFETY ANALYSIS REPORTS High definition images inspection technique expectation-maximisation algorithm damage assessment Parameter estimation Dictionaries as Topic environmental monitoring inspection methods Earthquakes

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EMDC: A Semi-supervised Approach for Word Alignment 10

EMDC: A Semi-supervised Approach for Word Alignment

引用

International conference on computational linguistics

作者： Qin Gao Francisco Guzman Stephan Vogel Carnegie Mellon University Centro de Sistemas Inteligentes

ISBN: (纸本)9787302234562

Abstract This paper proposes a novel semisupervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find high precision partial alignments that serve as constraints for a generative aligner which implements a constrained version of the EM algorithm. Experiments on small-size Chinese and Arabic tasks show consistent improvements on AER. We also experimented with moderate-size Chinese machine translation tasks and got an average of 0.5 point improvement on BLEU scores across five standard NIST test sets and four other test sets.

关键词： Belgo-Luxembourg Economic Union ANION EXCHANGE RESIN TEST SETS Word Religious Missions Alignment expectation-maximisation algorithm Chinese machine translation Chinese philosophy Chinese art

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fMRI Time-Series Clustering Using a Mixture of Mixtures of Student's-t and Rayleigh Distributions

fMRI Time-Series Clustering Using a Mixture of Mixtures of S...

引用

European Signal Processing Conference

作者： Qunyi Xie Zhuyan Zhang Xu Pan Hongqing Zhu School of Information Science & Engineering East China University of Science and Technology

ISBN: (纸本)9781509018918

In this paper, a new Markov random field-based mixture model, where each of its components is a mixture of Student's-t and Rayleigh distributions, is proposed for clustering fMRI time-series. By introducing the non-symmetric Rayleigh distribution, the proposed algorithm has flexibility to fit various types of observed time-series. Moreover, our method incorporates Markov random field so that the spatial relationships between neighboring voxels are considered, which makes the presented model more robust to noise, and that preserves more details of the clustering results compared with other symmetric distribution-based algorithms. Additionally, the expectation maximization algorithm is directly implemented to estimate the parameter set by maximizing the data log-likelihood function. The proposed framework is evaluated on real fMRI time-series, and the quantitatively compared results are demonstrated in terms of effectiveness and accuracy.

关键词： mixtures Rayleigh distribution fMRI expectation-maximisation algorithm Rayleigh Markov random fields Cluster Analysis Data logging Flexibility log-likelihood function

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A SPARSE COMPONENT MODEL OF SOURCE SIGNALS AND ITS APPLICATION TO BLIND SOURCE SEPARATION

A SPARSE COMPONENT MODEL OF SOURCE SIGNALS AND ITS APPLICATI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yu Kitano Hirokazu Kameoka Yosuke Izumi Nobutaka Ono Shigeki Sagayama Graduate School of Information Science and Technology The University of Tokyo Japan NTT Communication Science Laboratories NTT Corporation

ISBN: (纸本)9781424442959

In this paper, we propose a new method of blind source separation (BSS) for music signals. Our method has the following characteristics: 1) the method is a combination of the sparseness-based model of source signals and the factorized basis model in nonnegative matrix factorization (NMF), 2) it is assumed that only one basis which structure source signals is active at each time-frequency bin of the observed signals, in order to degrade the degree of freedom, 3) parameter estimation algorithm is based on the EM algorithm regarding the index of the only one active basis as the hidden variable. We develop the formulation at a different point from NMF and show source separation performance in some simulation experiments.

关键词： Blind source separation nonnegative matrix factorization EM algorithm sparseness of source signals time-frequency masking Blind source separation expectation-maximisation algorithm signals matrix decomposition Source separation Phase Mask NMF protocol

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A vessel segmentation method for MRA data based on probabilistic mixture model

A vessel segmentation method for MRA data based on probabili...

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2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015)

作者： Pei Lu Cheng Wang Shoujun Zhou Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Accurate segmentation and visualization of cerebral vessels have important significance to the diagnosis and treatment of relevant brain diseases. However, some segmentation algorithms are only competent for the standard data. In this paper, a segmentation method based on probabilistic mixture model is proposed to solve clinical problems. Through histogram analysis of the magnetic resonance angiography (MRA) data offered by Guangzhou General Hospital of the Chinese PLA, a mixture model formed by six probabilistic distributions (one Exponential, one Rayleigh, and four Normal distributions) was built to fit the histogram curve. Least squares and expectation maximization (EM) algorithms have been used for parameters estimation. At last, the segmentation was enhanced by maximum a posteriori probability (MAP) and Markov random field (MRF) algorithm. The effectiveness of the proposed method has been validated by segmentation tests on a series of clinical MRA data with good performance.

关键词： EM algorithm MRF Vessel segmentation histogram analysis mixture model Mixture models expectation-maximisation algorithm Probability histograms MYRF gene Maximum a posteriori estimation Parameter estimation cerebrovascular system

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