Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (EC...
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Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time-frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.
In this paper a novel algorithm for estimating the parametric form of the camera motion is proposed. A novel stochastic vector field model is presented which can handle smooth motion patterns derived from long periods...
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ISBN:
(纸本)9789898111210
In this paper a novel algorithm for estimating the parametric form of the camera motion is proposed. A novel stochastic vector field model is presented which can handle smooth motion patterns derived from long periods of stable camera movement and also can cope with rapid motion changes and periods where camera remains still. A set of rules for robust and online updating of the model parameters is also proposed, based on the expectation maximization algorithm. Finally, we fit this model in a particle filters framework, in order to predict the future camera motion based on current and prior knowledge.
The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a region-based level-set approach an...
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ISBN:
(纸本)9781424433216
The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a region-based level-set approach and some conventional methods. Our proposed approach produces good results and decreases processing time. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.
In order to compensate for the weaknesses of the expectationmaximization (EM) algorithm to over-training and to improve model performance for new data, we have recently proposed aggregated EM (Ag-EM) algorithm that i...
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ISBN:
(纸本)9781424414833
In order to compensate for the weaknesses of the expectationmaximization (EM) algorithm to over-training and to improve model performance for new data, we have recently proposed aggregated EM (Ag-EM) algorithm that introduces bagging-like approach in the framework of the EM algorithm and have shown that it gives similar improvements as cross-validation EM (CV-EM) over conventional EM. However, a limitation with the experiments was that the number of multiple models used in the aggregation operation or the ensemble size was fixed to a small value. Here, we investigate the relationship between the ensemble size and the performance as well as giving a theoretical discussion with the order of the computational cost. The algorithm is first analyzed using simulated data and then applied to large vocabulary speech recognition on oral presentations. Both of these experiments show that Ag-EM outperforms CV-EM by using larger ensemble sizes.
Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the obj...
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ISBN:
(纸本)9780819472960
Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.
In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modelin...
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ISBN:
(纸本)9780892082797
In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modeling the distribution of a novel intensity projection technique using a mixture of K Gaussians. Model parameters are then estimated using the expectationmaximization (EM) algorithm. Using the proposed algorithm, halftone areas were successfully differentiated from text regions
A statistical object detection and tracking mutual feedback scheme, combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectationmaximization (EM) Kalman filter algorithm, is prop...
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ISBN:
(纸本)9781424435296
A statistical object detection and tracking mutual feedback scheme, combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectationmaximization (EM) Kalman filter algorithm, is proposed in this paper. In space object detection stage, PCA provides compact and decorrelated feature space, the tracked object feature is statistically represented as GMM in RGB color space, objects are detected by maximum a posteriori (MAP) estimation. In temporal tracking stage, the tracked object is determined by the Bhattacharyya similarity measurement, the object position of consecutive frame is predicted by EM Kalman filter algorithm. The integration of object detection and tracking spatio-temporal mutual feedback scheme can decrease the accumulation error. We have applied the proposed method to object detection and tracking under the partial occlusion and the changes of moving speed with encouraging results.
Background: The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster ...
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Background: The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results: In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine ( divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenoty
We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesia...
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We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesian probability theory, the detection of action potentials is posed as a model selection problem. Our technique provides a robust performance over a wide range of simulated conditions, and compares favorably to selected supervised and unsupervised detection techniques.
We propose a joint MAP channel estimation and data detection technique based on the expectationmaximization (EM) method with paralel interference cancelation (PIC) for downlink multicarrier (MC) code division multipl...
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We propose a joint MAP channel estimation and data detection technique based on the expectationmaximization (EM) method with paralel interference cancelation (PIC) for downlink multicarrier (MC) code division multiple access (CDMA) systems in the presence of frequency selective channels. The quality of multiple access interference (MAI), which can be improved by using channel estimation and data estimation of all active users, affects considerably the performance of PIC detector. Therefore, data and channel estimation performance obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. The EM algorithm derived estimates the complex channel parameters of each subcarrier iteratively and generates the soft information representing the data a posterior probabilities. The soft information is then employed in a PIC module to detect the symbols efficiently. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach is studied in terms of bit-error rate (BER) and mean square error (MSE). Throughout the simulations, extensive comparisons with previous works in literature are performed, showing that the new scheme can offer superior performance.
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