Results 41 to 50 of about 62,966 (184)
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models
BMC Bioinformatics, 2006 Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about ...
Borodovsky Mark +2 more
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SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules. [PDF]
PLoS ONE, 2016 The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods ...
Haitao Guo, Hongwei Huo, Qiang Yu
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Frontiers in Robotics and AI, 2022 In this study, HcVGH, a method that learns spatio-temporal categories by segmenting first-person-view (FPV) videos captured by mobile robots, is proposed.
Masatoshi Nagano +5 more
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Informational and Causal Architecture of Continuous-time Renewal and Hidden Semi-Markov Processes
, 2016 We introduce the minimal maximally predictive models ({\epsilon}-machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions ...
Crutchfield, James P., Marzen, Sarah E.
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Structure and Randomness of Continuous-Time Discrete-Event Processes
, 2017 Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process.
Crutchfield, J. P., Marzen, S. E.
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A new model-discriminant training algorithm for hybrid NN-HMM systems [PDF]
, 1996 This paper describes a hybrid system for continuous speech recognition consisting of a neural network (NN) and a hidden Markov model (HMM). The system is based on a multilayer perceptron, which approximates the a-posteriori probability of a sequence of ...
Caspary, P., Reichl, W., Ruske, G.
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Introduction to Hidden Semi-Markov Models
, 2018 The purpose of this volume is to present the theory of Markov and semi-Markov processes in a discrete-time, finite-state framework. Given this background, hidden versions of these processes are introduced and related estimation and filtering results developed. The approach is similar to the earlier book, Elliott et al. (1995).
John van der Hoek, Robert J. Elliott
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MathematicsRegime switching models have been widely studied for their ability to capture the dynamic behavior of time series data and are widely used in economic and financial data analysis.
Zhenni Tan, Yuehua Wu
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Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition
International Journal of Distributed Sensor Networks, 2018 Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care.
Yang Sung-Hyun +3 more
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Global discriminative learning for higher-accuracy computational gene prediction. [PDF]
PLoS Computational Biology, 2007 Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content.
Axel Bernal +3 more
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