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20th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Bacci, Giovanni Ingolfsdottir, Anna Larsen, Kim G. Reynouard, Raphael Dept Comp Sci Aalborg Denmark Dept Comp Sci Reykjavik Iceland

ISBN: (纸本)9781665443371

Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective in the deployment of safety-critical CPSs. Central for a successful application of such techniques is the construction of an accurate formal model for the system. Manual construction can be a resource-demanding and error-prone process, thus motivating the design of automata learning algorithms to synthesise a system model from observed system behaviours. This paper revisits and adapts the classic baum-welch algorithm for learning Markov decision processes and Markov chains. For the case of MDPs, which typically demand more observations, we present a model-based active learning sampling strategy that choses examples which are most informative w.r.t. the current model hypothesis. We empirically compare our approach with state-of-the-art tools and demonstrate that the proposed active learning procedure can significantly reduce the number of observations required to obtain accurate models.

关键词： baum-welch algorithm Markov decision processes active learning

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Refined Learning of Hidden Markov Models with a Modified baum-welch algorithm and Informative Components

Refined Learning of Hidden Markov Models with a Modified Bau...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Michael G.Madden School of Engineering and Informatics National University of Ireland Galway GalwayIreland

Methods for refined learning of hidden Markov models(HMMs) have been studied for decades,taking either structural or parametric *** paper discusses using informative components of state parameters to refine the training of *** the purpose of HMM learning is to decompose a high-dimensional observation space into latent states,we may still end up with high dimensional state models.A problem with such high dimensional state models is that if they are learned with limited amounts of training data,this will bias the likelihood during training,particularly when there are outliers in the observation *** propose a modified baum-welch algorithm to estimate the HMM parameters over informative components selected with nearly sufficient statistics, using relative entropy *** method produces a modified likelihood estimation in reduced dimensions,which decreases the computation complexity and preserves near-optimality of the HMM that is *** variations on the algorithm are discussed,one that deals with general state model and another that considers a special Gaussian *** approach is evaluated with a problem of using pen tip trajectory for handwriting character verification and *** data sets are studied,one with low dimensionality and the other with high dimensionality.A comparison is performed of the results of using HMMs trained without and with informative state *** both data sets,we found that our approach yields improvements in *** error rate varies with relative entropy change is also evaluated.

关键词： hidden Markov model baum-welch algorithm relative entropy handwriting recognition

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Statistical and computational guarantees for the baum-welch algorithm

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Fanny Yang Sivaraman Balakrishnan Martin J. Wainwright Department of Electrical Engineering and Computer Sciences University of California Berkeley CA Department of Statistics Carnegie Mellon University Pittsburgh PA Department of Statistics Department of Electrical Engineering and Computer Sciences University of California Berkeley CA

The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete time series, with applications including speech recognition, computational biology, computer vision and econometrics. Estimating an HMM from its observation process is often addressed via the baum-welch algorithm, which is known to be susceptible to local optima. In this paper, we first give a general characterization of the basin of attraction associated with any global optimum of the population likelihood. By exploiting this characterization, we provide non-asymptotic finite sample guarantees on the baum-welch updates and show geometric convergence to a small ball of radius on the order of the minimax rate around a global optimum. As a concrete example, we prove a linear rate of convergence for a hidden Markov mixture of two isotropic Gaussians given a suitable mean separation and an initialization within a ball of large radius around (one of) the true parameters. To our knowledge, these are the first rigorous local convergence guarantees to global optima for the baum-welch algorithm in a setting where the likelihood function is nonconvex. We complement our theoretical results with thorough numerical simulations studying the convergence of the baum-welch algorithm and illustrating the accuracy of our predictions.

关键词： baum-welch algorithm EM algorithm Hidden Markov models graphical models non-convex optimization

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Tensor-Based baum-welch algorithms in Coupled Hidden Markov Model for Responsible Activity Prediction

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 2924-2937页

作者： Zhang, Shunli Yang, Laurence T. Zhang, Yue Lu, Zhixing Yu, Jing Cui, Zongmin Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber-physical-social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based baum-welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.

关键词： Hidden Markov models Prediction algorithms Tensors Artificial intelligence Data models Analytical models Algebra Activity prediction baum-welch algorithm learning problem of tensor-based couple hidden Markov model (T-CHMM) responsible AI tensor algebra

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HMM machine learning and inference for Activities of Daily Living recognition

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JOURNAL OF SUPERCOMPUTING 2010年第1期54卷 29-42页

作者： Cheng, Bo-Chao Tsai, Yi-An Liao, Guo-Tan Byeon, Eui-Seok Natl Chung Cheng Univ Dept Commun Engn Chiayi 621 Taiwan Sun Moon Univ Dept Ind & Management Engn Asan 336740 Chungnam South Korea

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person's functional status. Many recent researches concentrate on designing a good Context Aware Computing System to automate the actions necessarily triggered by ADL recognitions. Implementing a correct ADL recognition engine is a hard work, but will repay the system with lower inference errors and higher system dependability. A good ADL recognition engine is required to adjust its inference strategy based on the learning capability in order to avoid a high error rate, especially in real world inputs with a significant difference as compared to those in the training phase. In this paper, we proposed a powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden Markov Model (ALHMM), which combines the Viterbi and baum-welch algorithms to enhance the accuracy and learning capability. The assessments of ALHMM are conducted on the Python platform and show the practical feasibility of Activity Recognition in residential homes. Such a technique can provide the key answer required for advancing the state-of-the-art in context-aware computing and applications in real life.

关键词： Activity Recognition ADL Context Aware Computing System Hidden Markov Model Viterbi algorithm baum-welch algorithm

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Reliability Analysis of Phasor Measurement Unit Using Hidden Markov Model

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IEEE SYSTEMS JOURNAL 2014年第4期8卷 1293-1301页

作者： Murthy, Cherukuri Mishra, Anvesha Ghosh, Debomita Roy, Diptendu Sinha Mohanta, Dusmanta Kumar Natl Inst Sci & Technol Dept Elect & Elect Engn Berhampur 761008 Orissa India IBM India Pvt Ltd Gurgaon 122002 India Birla Inst Technol Dept Elect & Elect Engn Mesra 835215 Ranchi India Natl Inst Sci & Technol Dept Comp Sci Engn Berhampur 761008 Orissa India

Recently, electrical power systems have emerged as the largest and most complex machine because of its inordinate growth due to technological innovations and because of geographical sprawl. This growth has necessitated a commensurate system for operation, monitoring, and control, which is known as the wide-area measurement system (WAMS). The edifice of WAMS is the phasor measurement unit (PMU). Its failure adversely affects WAMS as a whole;therefore, reliability analysis of the PMU is extremely essential. This paper uses hidden Markov model to perform the reliability analysis of PMU. Although many reliability evaluation methods have been used for computing steady-state probabilities, the proposed methodology has the unique ability to compute transient probability, leading to better system monitoring during transient states to ensure proper restorative initiatives. This paper validates the efficacy of the proposed methodology with relevant case studies for real-time application in a modern power system to ensure reliable monitoring using PMUs.

关键词： baum-welch algorithm hidden Markov model (HMM) phasor measurement unit (PMU) reliability Viterbi algorithm wide-area measurement system (WAMS)

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Hidden Markov models for burst error characterization in indoor radio channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 1997年第4期46卷 1006-1020页

作者： GarciaFrias, J Crespo, PM TELEFON I&D MADRID 28043 SPAIN

Many digital communication channels exhibit statistical dependencies among errors, The design of error control schemes for such channels and their performance evaluation is simplified if appropriate generative models of the overall communication link are available, This paper presents a new class of generative models based on the interconnection of hidden Markov submodels parameterized by the baum-welch algorithm, The method has some resemblance to the well-studied problem of speech recognition of isolated words;however, in our approach, instead of dealing with words, one deals with error bursts, and the final goal is to generate bursts rather than to recognize words, The proposed model is particularly suitable for simulating error profiles with long bursts, as is often the case in indoor radio channels, where the error-free gaps inside a burst are heavily nonrenewal. The merits of the method are corroborated by applying the technique to two particular examples of indoor code-division multiple-access (CDMA) radio links.

关键词： baum-welch algorithm generative channel models hidden Markov models simulation of radio channel error bursts

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APPLICATION OF HIDDEN MARKOV-MODELS FOR SIGNATURE VERIFICATION

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PATTERN RECOGNITION 1995年第2期28卷 161-170页

作者： YANG, L WIDJAJA, BK PRASAD, R DELFT UNIV TECHNOL TELECOMMUN & TRAFF CONTROL SYST GRP2600 GA DELFTNETHERLANDS

This paper describes a technique for on-line signature verification using Hidden Markov Models (HMMs). Signatures are captured and digitized in real-time using a graphic tablet. For each signature a HMM is constructed using a set of sample signatures described by the normalized directional angle Function of the distance along the signature trajectory. The baum-welch algorithm is used for both training and classification. Experimental results based on 496 signatures from 31 subjects are presented which show that HMM technique is very potential for signature verification.

关键词： HMMS baum-welch algorithm FORWARD PROBABILITY BACKWARD PROBABILITY SIGNATURE VERIFICATION

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Training hidden Markov models with multiple observations - A combinatorial method

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2000年第4期22卷 371-377页

作者： Li, XL Parizeau, M Plamondon, R CADlink Technol Corp Ottawa ON K1H 1E1 Canada Univ Laval Dept Genie Elect & Genie Informat St Foy PQ G1K 7P4 Canada Ecole Polytech Montreal PQ H3C 3A7 Canada

Hidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and Versatility in handling sequential signals. On the other hand, as these models have a complex structure and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years researchers have used Levinson's training equations in speech and handwriting applications, simply assuming that all observations are independent of each other. This paper presents a formal treatment of HMM multiple observation training without imposing the above assumption. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependence-independence assumptions. By generalizing baum's auxiliary function into this framework and building up an associated objective function using the Lagrange multiplier method, it is proven that the derived training equations guarantee the maximization of the objective function. Furthermore, we show that Levinson's training equations can be easily derived as a special case in this treatment.

关键词： hidden Markov model forward-backward procedure baum-welch algorithm multiple observation training

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First and Second-Order Semi-Hidden Fritchman Markov models for a multi-carrier based indoor narrowband power line communication system

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PHYSICAL COMMUNICATION 2018年 29卷 55-66页

作者： Familua, Ayokunle Damilola Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn 1 Jan Smut Ave Johannesburg South Africa

The realization of a Semi-Hidden Fritchman Markov models (SHFMMs) for power line communication (PLC) channel is only practicable if combined with efficient algorithms for learning and inference. This article thus reports First and Second-Order SHFMMs for the bit error pattern at the output of an Orthogonal Frequency Division Multiplexing based system for in-home narrowband PLC applications. Accurate SHFMMs have been derived for a residential and laboratory environment using the efficient iterative baum-welch algorithm. The log-likelihood ratio plots, the error-free run distribution plots, the mean square error and Chi-Square (chi(2)) test validates the accuracy of the derived models. The reliability and accuracy of resulting models are confirmed by a close match between the measured bit error sequences and the model generated bit error sequences. The estimated Second-Order SHFMMs have been validated to be superior to the First-Order models, although at the expense of more computational complexity. Resulting models assist designers to speed up and facilitate the process of designing and evaluating novelties for PLC systems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： baum-welch algorithm Discrete channel models Power line communication OFDM Semi-Hidden Fritchman Markov model USRP

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