Relevant bibliographies by topics / Baum-Welch algorithm

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Academic literature on the topic 'Baum-Welch algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Baum-Welch algorithm"

1

Assunção, Joaquim, Paulo Fernandes, and Jean-Marc Vincent. "Piecewise Aggregation for HMM Fitting: A Pre-Fitting Model for Seamless Integration with Time-Series Data." International Journal of Software Engineering and Knowledge Engineering 29, no. 11n12 (2019): 1835–50. http://dx.doi.org/10.1142/s0218194019400242.

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We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.
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2

Zhang, Xu, Ting Wu, Qiuhua Zheng, et al. "Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models." Sensors 22, no. 8 (2022): 2874. http://dx.doi.org/10.3390/s22082874.

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Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum–Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum–Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.
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3

Zhang, Jingwen, Fanggang Wang, Zhangdui Zhong, and Shilian Wang. "Continuous Phase Modulation Classification via Baum-Welch Algorithm." IEEE Communications Letters 22, no. 7 (2018): 1390–93. http://dx.doi.org/10.1109/lcomm.2018.2821171.

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4

Steeneck, Daniel, and Fredrik Eng-Larsson. "The Baum–Welch algorithm with limiting distribution constraints." Operations Research Letters 46, no. 6 (2018): 563–67. http://dx.doi.org/10.1016/j.orl.2018.08.008.

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5

GOLOD, DANIIL, and DANIEL G. BROWN. "A TUTORIAL OF TECHNIQUES FOR IMPROVING STANDARD HIDDEN MARKOV MODEL ALGORITHMS." Journal of Bioinformatics and Computational Biology 07, no. 04 (2009): 737–54. http://dx.doi.org/10.1142/s0219720009004242.

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In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the Baum–Welch algorithm, and we describe ways to improve their naïve implementations. For the Baum–Welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and [Formula: see text], with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe [Formula: see text] and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory.
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6

Xu, Hui Hong, and Su Chun Gao. "Speaker Recognition Study Based on Optimized Baum-Welch Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2471–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2471.

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The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.
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7

Zhang, Yanxue, Dongmei Zhao, and Jinxing Liu. "The Application of Baum-Welch Algorithm in Multistep Attack." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/374260.

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The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.
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8

Jensen, Jens Ledet. "A Note on the Linear Memory Baum-Welch Algorithm." Journal of Computational Biology 16, no. 9 (2009): 1209–10. http://dx.doi.org/10.1089/cmb.2008.0178.

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9

El annas, Monir, Mohamed Ouzineb, and Badreddine Benyacoub. "Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm." Statistics, Optimization & Information Computing 10, no. 1 (2022): 160–70. http://dx.doi.org/10.19139/soic-2310-5070-1213.

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Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.
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10

Anandhalekshmi, A. V., V. Srinivasa Rao, and G. R. Kanagachidambaresan. "Hybrid approach of baum-welch algorithm and SVM for sensor fault diagnosis in healthcare monitoring system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 2979–88. http://dx.doi.org/10.3233/jifs-210615.

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Internet of Things (IoT) based healthcare monitoring system is becoming the present and the future of the medical field around the world. Here the monitoring system acquires the regular health details of hospital discharged patients like elderly patients, patients out of critical operations, and patients from remote areas, etc., and transmits it to the doctors. But the system is highly susceptible to sensor faults. Hence a data-driven hybrid approach of Hidden Markov Model (HMM) based on baum-welch algorithm with Support Vector Machine (SVM) is proposed to predict the abnormality caused by the medical sensors. The proposed work first perform the abnormality detection on the sensor data using the HMM based on baum-welch algorithm in which the normal data is separated from abnormal data followed by classifying the abnormal data as critical patient data or sensor fault data using the SVM. Here the proposed work efficiently performs fault diagnosis with an overall accuracy of 99.94% which is 0.59% better than the existing SVM model. And also a comparison is made between the hybrid approach and the existing ML algorithms in terms of recall and F1-score where the proposed approach outperforms the other algorithms with a recall value of 100% and F1-score of 99.7%.
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