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

共 495 条记录，以下是301-310 订阅

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SRATS: Software Reliability Assessment Tool on Spreadsheet (Experience Report)

SRATS: Software Reliability Assessment Tool on Spreadsheet (...

引用

IEEE 24th International Symposium on Software Reliability Engineering Workshops (ISSREW)

作者： Okamura, Hiroyuki Dohi, Tadashi Hiroshima Univ Grad Sch Engn Dept Informat Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9781479923663

This paper presents a software reliability assessment tool which is built as a Microsoft Excel AddIn. The tool handles 11 types of software reliability growth models (SRGMs) and easily provides the maximum likelihood estimates (MLEs) from input data on spreadsheets. The tool consists of two components: the user interface written by Visual Basic for Application (VBA) and the dynamic link library (DLL) written by C language. In the DLL, the state-of-the-art parameter estimation algorithms for SRGMs are implemented, which are developed from the EM (expectation-maximization) algorithm. These algorithms enable us to derive the MLEs for any patterns of data.

关键词： C language Visual BASIC expectation-maximisation algorithm maximum likelihood estimation parameter estimation software reliability software tools spreadsheet programs user interfaces

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Markov chain Monte Carlo data association for target tracking 25

Markov chain Monte Carlo data association for target trackin...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Bergman, N Doucet, A Linkoping Univ Dept Elect Engn Div Automat Control SE-58183 Linkoping Sweden

ISBN: (纸本)0780362934

We consider the estimation of the state of a discrete-time Markov process using observations which are sets of measurements from a finite number of known linear models. The measurement to model association is unknown and false measurements that do not yield any information about the Markov process are contained in the measurement set. The objective is to perform data association between the detected measurements and the models and determine optimal estimates of the state of the Markov process. The application of this problem is found in over the horizon target tracking. We derive iterative deterministic and stochastic algorithms based on Gibbs sampling. Rao-Blackwellisation allows us to solve the problem efficiently, yielding methods with computational complexity linear in the number of recieved data sets. Contrary to recent approaches based on the EM algorithm, the novel procedures we propose do not require an introcution of a missing data set and consequently their range of applicability is wider. A simulation study shows that the new algorithms are superior to previously proposed methods.

关键词： Markov chain Target tracking data association Markov chain monte carlo methods State estimation expectation-maximisation algorithm Markov missing data beyond visual range

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Recognition of hand gesture based on Gaussian Mixture Model

Recognition of hand gesture based on Gaussian Mixture Model

引用

International Workshop on Content-Based Multimedia Indexing

作者： Jia, Jia Jiang, Jianmin Wang, Dong Univ Bradford Sch Informat EIMC Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9781424420438

This paper presents, a new method for gesture recognition of Human beings' hand. This method integrates the features of shape, color and orientation histograms, which are extracted from images, and estimate the comparability with all the different types of gestures by a proposed expectation-Maximization algorithm in Gaussian Mixture Model. The classification results were presented based on the values of likelihood compared with all the types of pre-assigned images, and the performance of this approach in an experiment is shown that the proposed method works well.

关键词： Gaussian processes expectation-maximisation algorithm feature extraction gesture recognition image classification image colour analysis Gaussian mixture model expectation-maximization algorithm hand gesture recognition orientation histograms preassigned Gesture recognition expectation-maximisation algorithm image colour analysis Gaussian processes Image classification Feature extraction Mixture models

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Skills Transfer Across Dissimilar Robots by Learning Context-Dependent Rewards

Skills Transfer Across Dissimilar Robots by Learning Context...

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

作者： Malekzadeh, Milad S. Bruno, Danilo Calinon, Sylvain Nanayakkara, Thrishantha Caldwell, Darwin G. IIT Dept Adv Robot Via Morego 30 I-16163 Genoa Italy Univ London King Coll Div Engn London WC1E 7HU England

ISBN: (纸本)9781467363587

Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problems in which the robot autonomously extracts an optimal reward function that defines the goal of the task. By relying on Gaussian mixture models, the proposed approach learns how the different candidate reward functions are combined, and in which contexts or phases of the task they are relevant for explaining the user's demonstrations. The extracted reward profile is then exploited to improve the skill with a self-refinement approach based on expectation-maximization, allowing the imitator to reach a skill level that goes beyond the demonstrations. The approach can be used to reproduce a skill in different ways or to transfer tasks across robots of different structures. The proposed approach is tested in simulation with a new type of continuum robot (STIFF-FLOP), using kinesthetic demonstrations from a Barrett WAM manipulator.

关键词： Gaussian processes expectation-maximisation algorithm learning (artificial intelligence) manipulators robot programming

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Online Quantum Mixture Regression for Trajectory Learning by Demonstration

Online Quantum Mixture Regression for Trajectory Learning by...

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

作者： Korkinof, Dimitrios Demiris, Yiannis Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2BT England

ISBN: (纸本)9781467363587

In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.

关键词： expectation-maximisation algorithm iterative methods learning (artificial intelligence) numerical stability regression analysis robots stochastic processes

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On-line Handwritten Mathematical Expression Recognition Method based on Statistical and Semantic Analysis

On-line Handwritten Mathematical Expression Recognition Meth...

引用

11th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Hu, Yang Peng, Liangrui Tang, Yejun Tsinghua Univ Dept Elect Engn Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781479932436

Recognition of handwritten mathematical expressions (HMEs) has become a cutting edge research topic recently, as there are increasingly needs for pen-inputting applications. In this paper, we presented a novel framework to analyse HME layout and semantic information. This framework includes three steps, namely symbol segmentation, symbol recognition and semantic relationship analysis. For symbol segmentation, a decomposition on strokes is operated, then dynamic programming is adopted to find the paths corresponding to the best segmentation manner and reduce the stroke searching complexity. For symbol recognition, spatial geometry and directional element features are classified by a Gaussian Mixture Model learnt through expectation-Maximization algorithm. At last, in the semantic relationship analysis module, a ternary tree is utilized to to store the ranked symbols through calculating the operator priorities. The motivation for our work comes from the apparent difference in writing styles across western and Chinese populations. Our results are reasonable and show promise on the private dataset.

关键词： Handwritten Mathematical Expression Online Recognition Statistical and Semantic Analysis on-line identification Semantics expectation-maximisation algorithm expression recognition Stroke Recognition (Psychology) dynamic programming segmentation Segmentation

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Applying Fuzzy EM algorithm with a Fast Convergence to GMMs

Applying Fuzzy EM Algorithm with a Fast Convergence to GMMs

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2010 IEEE World Congress on Computational Intelligence

作者： Ju, Zhaojie Liu, Honghai Univ Portsmouth Sch Creat Technol Intelligent Syst & Robot Grp Portsmouth PO1 2UP Hants England

ISBN: (纸本)9781424469208

Inspired from the mechanism of Fuzzy C-means (FCMs) which introduces a degree of fuzziness on the dissimilarity function based on distances, a fuzzy expectation Maximization (EM) algorithm for Gaussian Mixture Models (GMMs) is proposed in this paper. In the fuzzy EM algorithm, the dissimilarity function is defined as the multiplicative inverse of probability density function. Different from FCMs, the defined dissimilarity function is based on the exponential function of the distance. The fuzzy EM algorithm is compared with normal EM algorithm in terms of fitting degree and convergence speed. The experimental results in modeling random data and various characters demonstrate the ability of the proposed algorithm in reducing the computational cost of GMMs.

关键词： Gaussian mixture model Gaussian processes convergence convergence speed dissimilarity function expectation-maximisation algorithm exponential function fast convergence fitting degree fuzziness degree fuzzy EM algorithm fuzzy c-means fuzzy expectation maximization fuzzy set theory multiplicative inverse normal EM algorithm pattern clustering probability probability density function

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Supervised Topic Regression via Experts

Supervised Topic Regression via Experts

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Lin, Song Guo, Ping Xin, Xin Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781479914845

This paper focuses on the research issue of supervised topic models. Traditionally, an unsupervised topic model is typically supervised by incorporating a supervised linear operator. Although this kind of methods has successfully achieved supervised topic representations, as well as tractable computational complexity, the main limitation lies in that it assumes the topic data is linearly distributed. Therefore, when the practical data does not follow the linear property, the model cannot perform well. To solve this problem, a non-linear supervised topic model is proposed in this paper. Specifically, the mixture of experts (ME), as a kind of "divide and conquer", is utilized to deal with the regression of non-linear of topic data. We integrate the mixture of experts and unsupervised latent Dirichlet allocation (LDA) in a Bayesian manner. The proposed model can also reduce overfitting problem of ME with inputting high-dimensional data. An elegant learning algorithm is derived based on variational expectation maximization algorithm. Experimental results show that the proposed model has better predictive performances compared with usupervised topic models, and some state-of-the-art supervised topic regression models (including sLDA model and MedLDA model), on two textual datasets and one image dataset.

关键词： Professional personnel Regression (Psychology) expectation-maximisation algorithm Dataset

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Low-Complexity Leakage-Based Carrier Frequency Offset Estimation Techniques for OFDMA Uplink Systems

Low-Complexity Leakage-Based Carrier Frequency Offset Estima...

引用

IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Lee, Kilbom Moon, Sung-Hyun Lee, Inkyu Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781424456383

In this paper, we propose an efficient carrier frequency offset (CFO) estimation technique based on the space alternating generalized expectation-maximization (SAGE) for uplink orthogonal frequency division multiple access (OFDMA) systems. In general, the SAGE method transforms a multidimensional search problem into a sequence of one-dimensional searches, which greatly simplifies the estimation procedure. However, the conventional algorithms based on the SAGE method require a large amount of computations to estimate the CFO due to exhaustive grid search. To reduce the computational burden, we exploit the leakage on the fast Fourier transform (FFT) output of the received signal after the multiple access interference is removed by the SAGE method. Then, this leakage-based approach reduces the complexity of the conventional SAGE algorithm regardless of an employed carrier assignment scheme by avoiding grid search. Simulation results show that our modified SAGE algorithm approaches the Cramer Rao bound at all signal to noise ratio (SNR) region with greatly reduced complexity compared to the conventional SAGE algorithms.

关键词： Cramer Rao bound OFDM modulation OFDMA uplink systems carrier frequency offset estimation estimation theory exhaustive grid search expectation-maximisation algorithm fast Fourier transform fast Fourier transforms frequency division multiple access interfe

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Unsupervised Motion Learning from a Moving Platform

Unsupervised Motion Learning from a Moving Platform

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IEEE Intelligent Vehicles Symposium

作者： Romero-Cano, Victor Nieto, Juan I. Agamennoni, Gabriel Univ Sydney Australian Ctr Field Robot Sydney NSW 2006 Australia

ISBN: (纸本)9781479907946;9781479907953

Learning motion patterns in dynamic environments is a key component of any context-aware robotic system, and probabilistic mixture models provide a sound framework for mining these patterns. This paper presents an approach for learning motion models from trajectories provided by the tracking system of a moving platform. We present a learning approach in which a Linear Dynamical System (LDS) is augmented with a discrete hidden variable that has a number of states equal to the number of behaviours in the environment. As a result, a mixture of linear dynamical systems (MLDSs) capable of explaining several motion behaviours is developed. The model is learned by means of the expectation Maximization (EM) algorithm.

关键词： data mining driver information systems expectation-maximisation algorithm unsupervised learning

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