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

共 493 条记录，以下是211-220 订阅

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Mixture Periodic GARCH models: Applications to Exchange Rate Modeling

Mixture Periodic GARCH models: Applications to Exchange Rate...

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5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Hamdi, Faycal Souam, Said Facult&#x00E9 de Math&#x00E9

ISBN: (纸本)9781467358149;9781467358125

In this paper, we propose an extension of a mixture periodic ARCH model (MPARCH) to a mixture periodic GARCH model (MPGARCH), and provide some probabilistic properties of this class of models. An estimation method based on the expectation-Maximization (EM) algorithm is proposed. Finally, it is applied to model the spot rates of the Algerian Dinar against the U.S.-Dollar and Euro. The empirical analysis demonstrates that the proposed mixture model yields the best performance among the competing models.

关键词： autoregressive processes exchange rates expectation-maximisation algorithm financial management probability

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Alignment of Binocular-Binaural Data Using a Moving Audio-Visual Target

Alignment of Binocular-Binaural Data Using a Moving Audio-Vi...

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15th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Khalidov, Vasil Forbes, Florence Horaud, Radu INRIA Grenoble Rhone Alpes 655 Ave Europe F-38330 Montbonnot St Martin France Idiap Res Inst Ctr Parc CH-1920 Martigny Switzerland

ISBN: (纸本)9781479901258

In this paper we address the problem of aligning visual (V) and auditory (A) data using a sensor that is composed of a camera-pair and a microphone-pair. The original contribution of the paper is a method for AV data aligning through estimation of the 3D positions of the microphones in the visual-centred coordinate frame defined by the stereo camera-pair. We exploit the fact that these two distinct data sets are conditioned by a common set of parameters, namely the (unknown) 3D trajectory of an AV object, and derive an EM-like algorithm that alternates between the estimation of the microphone-pair position and the estimation of the AV object trajectory. The proposed algorithm has a number of built-in features: it can deal with A and V observations that are misaligned in time, it estimates the reliability of the data, it is robust to outliers in both modalities, and it has proven theoretical convergence. We report experiments with both simulated and real data.

关键词： audio-visual systems cameras expectation-maximisation algorithm microphones robot vision stereo image processing

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On Improving the Extrapolation Capability of Task-Parameterized Movement Models

On Improving the Extrapolation Capability of Task-Parameteri...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Calinon, Sylvain Alizadeh, Tohid Caldwell, Darwin G. IIT Dept Adv Robot I-16163 Genoa Italy

ISBN: (纸本)9781467363587

Gestures are characterized by intermediary or final landmarks (real or virtual) in task space or joint space that can change during the course of the motion, and that are described by varying accuracy and correlation constraints. Generalizing these trajectories in robot learning by imitation is challenging, because of the small number of demonstrations provided by the user. We present an approach to statistically encode movements in a task-parameterized mixture model, and derive an expectation-maximization (EM) algorithm to train it. The model automatically extracts the relevance of candidate coordinate systems during the task, and exploits this information during reproduction to adapt the movement in real-time to changing position and orientation of landmarks or objects. The approach is tested with a robotic arm learning to roll out a pizza dough. It is compared to three categories of task-parameterized models: 1) Gaussian process regression (GPR) with a trajectory models database;2) Multi-streams approach with models trained in several frames of reference;and 3) Parametric Gaussian mixture model (PGMM) modulating the Gaussian centers with the task parameters. We show that the extrapolation capability of the proposed approach outperforms existing methods, by extracting the local structures of the task instead of relying on interpolation principles.

关键词： Gaussian processes adaptive control expectation-maximisation algorithm extrapolation learning by example learning systems manipulators motion control regression analysis trajectory control

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THE LINEAR PROCESS MIXTURE MODEL

THE LINEAR PROCESS MIXTURE MODEL

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23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Palmer, Jason A. Kreutz-Delgado, Ken Makeig, Scott Univ Calif San Diego Swartz Ctr Computat Neurosci La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781479911806

We consider a likelihood framework for analyzing multivariate time series as mixtures of independent linear processes. We propose a flexible, Newton algorithm for estimating impulse response functions associated with independent linear processes and an EM-based finite mixture model to handle intermittent regimes. Simulations and application to EEG are also provided.

关键词： Newton method electroencephalography expectation-maximisation algorithm medical signal processing time series

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A Multi-Objective Genetic Graph-based Clustering algorithm with Memory Optimization

A Multi-Objective Genetic Graph-based Clustering Algorithm w...

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IEEE Congress on Evolutionary Computation

作者： Menendez, Hector D. Barrero, David F. Camacho, David Univ Autonoma Madrid Dept Comp Sci E-28049 Madrid Spain Univ Alcala de Henares Dept Automat E-28871 Alcala De Henares Spain

ISBN: (纸本)9781479904549;9781479904532

Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.

关键词： data analysis data mining expectation-maximisation algorithm genetic algorithms graph theory learning (artificial intelligence) pattern clustering

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Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series

Learning Outcome-Discriminative Dynamics in Multivariate Phy...

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35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Nemati, Shamim Lehman, Li-wei H. Adams, Ryan P. Harvard Univ Sch Engn & Appl Sci 33 Oxford St Cambridge MA 02138 USA

ISBN: (纸本)9781457702167

Model identification for physiological systems is complicated by changes between operating regimes and measurement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is generated by switching among a finite collection of physiologically-constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.

关键词： cardiology expectation-maximisation algorithm learning systems medical signal processing multivariable systems physiology signal classification time series

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Robust Pose-Graph Loop-Closures with expectation-Maximization

Robust Pose-Graph Loop-Closures with Expectation-Maximizatio...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Lee, Gim Hee Fraundorfer, Friedrich Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Comp Vis & Geometry Lab Zurich Switzerland

ISBN: (纸本)9781467363587

In this paper, we model the robust loop-closure pose-graph SLAM problem as a Bayesian network and show that it can be solved with the Classification expectation-Maximization (EM) algorithm. In particular, we express our robust pose-graph SLAM as a Bayesian network where the robot poses and constraints are latent and observed variables. An additional set of latent variables is introduced as weights for the loop-constraints. We show that the weights can be chosen as the Cauchy function, which are iteratively computed from the errors between the predicted robot poses and observed loop-closure constraints in the expectation step, and used to weigh the cost functions from the pose-graph loop-closure constraints in the Maximization step. As a result, outlier loop-closure constraints are assigned low weights and exert less influences in the pose-graph optimization within the EM iterations. To prevent the EM algorithm from getting stuck at local minima, we perform the EM algorithm multiple times where the loop constraints with very low weights are removed after each EM process. This is repeated until there are no more changes to the weights. We show proofs of the conceptual similarity between our EM algorithm and the M-Estimator. Specifically, we show that the weight function in our EM algorithm is equivalent to the robust residual function in the M-Estimator. We verify our proposed algorithm with experimental results from multiple simulated and real-world datasets, and comparisons with other existing works.

关键词： SLAM (robots) belief networks expectation-maximisation algorithm image classification optimisation pose estimation robot vision

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Design & Full-wave EM Simulation of Ku-Band Orthomode Transducer (OMT)

Design & Full-wave EM Simulation of Ku-Band Orthomode Transd...

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10th International Bhurban Conference on Applied Science and Technology (IBCAST)

作者： Latif, Muhammad Salfl, Abaid Ullah Shafique, Muhammad Qamar SUPARCO Satellite Res & Dev Ctr Lahore Pakistan

ISBN: (纸本)9781467344265;9781467344258

The paper describes the design and full-wave EM simulation of Ku-band (10.7-14.5) GHz orthomode transducer (OMT). The OMT designed is class 2 (symmetric OMT) which has higher fractional bandwidth and cross-polarization isolation than the class 1 OMT. It is realized with Modified B phi ifot junction where the tuning pins are replaced with well placed capacitive steps. Moreover septum is four times thicker than conventional class 2 OMT, making it realizable up to 1 THz frequency. Cross-polarization isolation >= 80 dB and return loss >= 20 dB are achieved in the entire band. Effect of manufacturing tolerances and assembly misalignment are incorporated d the full-wave EM simulation. All the simulation & optimization work is done in industry standard EM Simulation software.

关键词： expectation-maximisation algorithm polarisation transducers

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Multiple surface range Estimation in 3D Flash imaging ladar via expectation maximization

Multiple surface range Estimation in 3D Flash imaging ladar ...

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10th International Bhurban Conference on Applied Science and Technology (IBCAST)

作者： Zhao Wen Han Shaokun Beijing Inst Technol Sch Optoelect Beijing 100081 Peoples R China

ISBN: (纸本)9781467344265;9781467344258

Multiple surface range estimation has become a challenging in 3D Flash imaging ladar due to the received pulse is corrupted by many factors, such as the optical aberrations from the imaging system, the noise from the detector and the medium through which it is imaging. These factors either cause the scene's intensity to spread, or blur, across pixel or add unwanted and disruptive noise effects. The intensity spreading and noise corrupts the correct pixel intensities at each range gate by mixing intensities with neighboring pixels thereby providing false intensity values and therefore incorrect photon counts to the range estimation. In this paper, we propose an algorithm to enhance the range estimation of multiple surfaces per pixel in 3D Flash imaging ladar. Using this algorithms, we can improve the multiple surface range estimation performance over traditional multiple surface range estimate algorithms. A comparison between the existing methods and the proposed algorithms has been carried out. The simulation results show the efficiency and effectiveness of proposed method over existing algorithms.

关键词： expectation-maximisation algorithm optical radar radar imaging

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Hybrid Identification of Time-varying Parameter with Particle Filtering and expectation Maximization

Hybrid Identification of Time-varying Parameter with Particl...

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21st Mediterranean Conference on Control and Automation (MED)

作者： Hartmann, Andras Vinga, Susana Lemos, Joao M. INESC ID R Alves Redol 9 P-1000029 Lisbon Portugal IST UTL P-1049001 Lisbon Portugal FCM UNL P-1169056 Lisbon Portugal

ISBN: (纸本)9781479909957;9781479909971

The problem of time-varying parameter identification is considered on a class of nonlinear hybrid systems. It is assumed that inputs and outputs are directly measured, and a subset of system parameters can take different values from a finite set at each time instance. An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems. The efficiency of the proposed method is illustrated through simulated examples.

关键词： expectation-maximisation algorithm nonlinear systems parameter estimation particle filtering (numerical methods) time-varying systems

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