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检索条件"主题词=Expectation-Maximization Algorithm"

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Research on Russian Cultural Transliteration algorithm Based on Hidden Markov Model 24

Research on Russian Cultural Transliteration Algorithm Based...

引用

International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Wang, Hongling Liu, Chunfu Heilongjiang Univ Technol Sch Int Educ Jixi Peoples R China

ISBN: (纸本)9798400718144

Transliteration of cultural terminology poses unique challenges in natural language processing, particularly for the Russian language with its rich cultural lexicon. This research addresses these challenges by developing a transliteration algorithm tailored to Russian cultural terms using a Hidden Markov Model (HMM). The proposed approach enhances the Speech Assessment Methods Phonetic Alphabet (SAMPA)-based phoneme set to account for the phonetic intricacies of cultural vocabulary, where stress and vowel reduction patterns play crucial roles. A specialized Russian cultural pronunciation dictionary containing over 10,000 terms, encompassing historical, literary, and folk terminology is involved. The proposed algorithm employs a data-driven methodology, starting with a "many-to-many" alignment using the expectation-maximization algorithm to accommodate the diverse phonetic representations of cultural terms. The alignment is further refined through a joint N-gram model and encapsulated into a robust HMM, which effectively models the probabilistic nature of phonetic variations. The decoding phase of the HMM accurately predicts the pronunciation of complex cultural terms. The algorithm's efficacy was evaluated through meticulous cross-validation, yielding an average word-form accuracy of 71.2% and a phoneme accuracy of 89.7%, demonstrating its potential in enhancing the accessibility and preservation of Russian cultural heritage in digital formats. CCS CONCEPTS similar to Computing methodologies similar to Machine learning similar to Machine learning algorithms similar to Dynamic programming for Markov decision processes

关键词： Cultural Transliteration Russian Hidden Markov Model SAMPA expectation-maximization algorithm

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The Optimal Hyperparameter for Bayesian Clustering and its Application to the Evaluation of Clustering Results 7

The Optimal Hyperparameter for Bayesian Clustering and its A...

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Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Yamazaki, Keisuke Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku G5-194259 Nagatsuta Yokohama Kanagawa Japan

ISBN: (纸本)9781479959556

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, which is the parameter of the prior distribution, and we propose an evaluation method for the clustering result, based on the prior optimization.

关键词： Bayes methods Gaussian distribution expectation-maximisation algorithm optimisation pattern clustering Bayesian clustering cluster analysis expectation-maximization algorithm maximum-likelihood method optimal hyperparameter parameter estimation parametric model prior distribution prior optimization probabilistic approach statistical property Accuracy Maximum likelihood estimation Optimization Probabilistic logic Shape

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Asset Life Prediction Using Multiple Degradation Indicators and Lifetime Data: a Gamma-based State Space Model Approach

Asset Life Prediction Using Multiple Degradation Indicators ...

引用

8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009)

作者： Zhou, Yifan Ma, Lin Mathew, J. Kim, H. Wolff, Rodney Queensland Univ Technol Sch Engn Syst CRC Integrated Engn Asset Management Brisbane Qld 4001 Australia Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia

ISBN: (纸本)9781424449033

This paper proposes a Gamma-based state space model to predict engineering asset life when multiple degradation indicators are involved and the failure threshold on these indicators are uncertain. Monte Carlo-based parameter estimation and model inference algorithms are developed to deal with the proposed Gamma-based state space model. A case study using real data from industry is conducted to compare the performance of the proposed model with the commonly used proportional hazard model (PHM). The result shows that the Gamma-based state space model is more appropriate to deal with the situation when the failure data is insufficient.

关键词： expectation-maximization algorithm Gamma process Proportional hazard model State space model

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Learning Finite Mixture of Ising Graphical Models

Learning Finite Mixture of Ising Graphical Models

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作者： Gu, Chong Western Michigan University

学位级别：Ph.D., Doctor of Philosophy

The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.

关键词： expectation-maximization algorithm Heterogeneous conditional dependencies Mixture of Ising graphical models Complex interactions Real-world example

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AN EM-algorithm APPROACH FOR THE DESIGN OF ORTHONORMAL BASES ADAPTED TO SPARSE REPRESENTATIONS

AN EM-ALGORITHM APPROACH FOR THE DESIGN OF ORTHONORMAL BASES...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Dremeau, A. Herzet, C. INRIA Ctr Rennes Bretagne Atlantique F-35000 Rennes France

ISBN: (纸本)9781424442966

In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.

关键词： Sparse representations dictionary learning expectation-maximization algorithm

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Combining Machine Learning and Information Retrieval Techniques for Software Clustering

Combining Machine Learning and Information Retrieval Techniq...

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1st International Workshop on Eternal Systems (EternalS 2011)

作者： Corazza, Anna Di Martino, Sergio Maggio, Valerio Scanniello, Giuseppe Univ Naples Federico II Naples Italy Univ Basilicata Potenza Italy

ISBN: (纸本)9783642280320

In the field of Software Maintenance the definition of effective approaches to partition a software system into meaningful subsystems is a longstanding and relevant research topic. These techniques are very important as they can significantly support a Maintainer in his/her tasks by grouping related entities of a large system into smaller and easier to comprehend subsystems. In this paper we investigate the effectiveness of combining information retrieval and machine learning techniques in order to exploit the lexical information provided by programmers for software clustering. In particular, differently from any related work, we employ indexing techniques to explore the contribution of the combined use of six different dictionaries, corresponding to the six parts of the source code where programmers introduce lexical information, namely: class, attribute, method and parameter names, comments, and source code statements. Moreover their relevance is estimated on the basis of the project characteristics, by applying a machine learning approach based on a probabilistic model and on the expectation-maximization algorithm. To group source files accordingly, two clustering algorithms have been compared, i.e. the K-Medoids and the Group Average Agglomerative Clustering, and the investigation has been conducted on a dataset of 9 open source Java software systems.

关键词： expectation-maximization algorithm Information Retrieval Probabilistic Model Remodularization Software Clustering Software Evolution

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Incremental Learning of Multivariate Gaussian Mixture Models

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Joint Conference of 20th Brazilian Symposium on Artificial Intelligence (SBIA) /SBRN/JRI

作者： Engel, Paulo Martins Heinen, Milton Roberto UFRGS Informat Inst BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9783642161377

This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.

关键词： Incremental Learning Unsupervised Learning Bayesian Methods expectation-maximization algorithm Finite Mixtures Clustering

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Kalman Filtering to Track Changes in Pupil Size for Automated Driving Systems 94

Kalman Filtering to Track Changes in Pupil Size for Automate...

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94th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Pillai, Prarthana Balasingam, Balakumar Jaekel, Arunita Biondi, Francesco Univ Windsor Dept Elect & Comp Engn Windsor ON Canada Univ Windsor Sch Comp Sci Windsor ON Canada Univ Windsor Dept Kinesiol Windsor ON Canada

ISBN: (纸本)9781665413688

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. These systems require human drivers to remain vigilant and maintain supervisory control over ADS. Therefore, the cognitive attention of the drivers must be estimated accurately for the safe adoption of ADS. Because of the non-invasive recording setup used in low-cost infrared eye-trackers, pupil size measurements are increasingly becoming applicable in the estimation of cognitive load. However, pupil size measurements are highly noisy, resulting in the poor classification of cognitive load levels. In this paper, we propose a methodology for improved classification of changes in cognitive load through the introduction of a state-space model-based approach to filter the pupil size data. The proposed approach was demonstrated on data collected from 16 participants while they performed driving task and several secondary tasks that are designed to emulate three different levels of driving distraction.

关键词： Cognitive load detection state-space model Kalman filter expectation-maximization algorithm eye-tracking human-computer interface physiological signals pupil size

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A distributed Kalman filter based fault detection scheme incorporating weighted average consensus algorithm for large-scale interconnected systems 12

A distributed Kalman filter based fault detection scheme inc...

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12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Yuan, Haozhou Gao, Jingjing Huang, Jian Yang, Xu Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Stimulated by the increasing demands for system safety and process reliability in complex industrial processes, this paper investigates a weighted average consensus algorithm based distributed fault detection scheme for large-scale interconnected systems, using a sensor network where each node is equipped with a Kalman filter (KF). To reduce the communication and computation efforts, the proposed distributed fault detection scheme is splitted into two phases: distributed offline training and online fault detection. To this end, the expectation-maximization (EM) algorithm is firstly addressed to identify the unknown measurement matrices and covariance matrices of noise vectors. It is followed by an average consensus algorithm so that the identical Kalman filters can be designed in parallel at all sensor nodes. On this basis, distributed residual generators and test statistics are constructed for fault detection purpose using the average consensus algorithm. Considering that there exist some special conditions, such as the occurrence of node failures, a variation of the distributed Kalman filter based fault detection scheme is proposed by dynamically adjusting the consensus weight. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through a case study on the waste water treatment plants (WWTPs).

关键词： Distributed fault detection Large-scale systems Distributed Kalman filter Weighted average consensus expectation-maximization algorithm

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A Novel Curved Gaussian Mixture Model and Its Application in Motion Skill Encoding

A Novel Curved Gaussian Mixture Model and Its Application in...

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

作者： Chen, Disi Li, Gongfa Zhou, Dalin Ju, Zhaojie Univ Portsmouth Fac Technol Sch Comp Portsmouth PO1 3HE Hants England Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Peoples R China

ISBN: (纸本)9781665417143

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability density function is needed to approximate a complex probability distribution. However, GMM cannot efficiently approach highly non-linear distributions. Thus, the proposed novel CGMM, as a weighted mixture of curved Gaussian models (CGM), is structured with nonlinear transfers, which reshapes the flat GMM into a geometrically curved one. As a consequence, CGMM has more freedoms and flexibilities than the flat GMM so a CGMM requires fewer number of components in fitting highly nonlinear motion trajectories. Moreover, we derive a dedicated iterative parameter estimation algorithm for the CGMM based on maximum likelihood estimation (MLE) theory. To evaluate the performance of the CGMM and its parameter estimation algorithm, a series of quantitative experiments are carried out. We first test the model performance in the data fitting task with the generated synthetic data. Then a motion skill encoding test is carried out on a human motion trajectory dataset built by a Virtual Reality (VR) based motion tracking system. The empirical results support that CGMM outperforms state-of-the-arts in the model performance test. Meanwhile, CGMM has a significant improvement in encoding high dimensional non-linear trajectory data compared to the GMM in motion skill encoding test with its dedicated parameter estimation algorithm.

关键词： Non-linear Transfer Curved Gaussian Mixture Model expectation-maximization algorithm Motion Skill Encoding

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