Results 71 to 80 of about 8,467,026 (293)
Sparseness Achievement in Hidden Markov Models [PDF]
14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007 In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irrelevant parameters are set exactly to zero. Alternatively to standard Maximum Likelihood Estimation (Baum Welch training), in the proposed approach the parameters estimation ...
Manuele Bicego +2 more
openaire +3 more sources
Multi‐Stage Social Value Orientation‐Based Lane‐Changing Driving for Autonomous Vehicles
Advanced Intelligent Systems, EarlyView.A multi‐stage SVO framework is developed to dynamically infer the social preferences of surrounding vehicles from human driving data. By combining offline learning, online SVO estimation, temporal intention tracking, and interaction‐aware planning, the framework enables autonomous vehicles to adapt lane‐changing decisions and trajectories to evolving ...
Nongrui Qin +4 more
wiley +1 more source
ADAPTIVE LEARNING OF HIDDEN MARKOV MODELS FOR EMOTIONAL SPEECH
Informatika, 2016 An on-line unsupervised algorithm for estimating the hidden Markov models (HMM) parame-ters is presented. The problem of hidden Markov models adaptation to emotional speech is solved.
A. V. Tkachenia
doaj
Algorithms, 2009 In this paper a method for selecting features for Human Activity Recognition from sensors is presented. Using a large feature set that contains features that may describe the activities to recognize, Best First Search and Genetic Algorithms are employed ...
Rodrigo Cilla +4 more
doaj +1 more source
A Reevaluation and Benchmark of Hidden Markov Models [PDF]
2014 14th International Conference on Frontiers in Handwriting Recognition, 2014 Hidden Markov models are frequently used in handwriting-recognitionapplications. While a large number of methodological variants have beendeveloped to accommodate different use cases, the core concepts have notbeen changed much. In this paper, we develop a number of datasets tobenchmark our own implementation as well as various other tool kits ...
Jean-Paul van Oosten, Lambert Schomaker
openaire +1 more source
American Journal of Botany, EarlyView.Abstract Premise Recent advances in genomic technologies now enable the reconstruction of evolutionary histories at fine spatial scales, illuminating the complex dynamics that shape biodiversity patterns in naturally discontinuous, archipelago‐like ecosystems such as cloud forests.
Leopoldo Hurtado‐Reveles +4 more
wiley +1 more source
An application of a semi-hidden Markov model in wireless communication systems
Mathematical Sciences, 2019 Stochastic processes are approved presentation of real systems which its development in space or time can be supposed as random. A semi-hidden Markov model as a type of stochastic processes is a modification of hidden Markov models with states that are ...
Shaghayegh Kordnoori +2 more
doaj +1 more source
Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R [PDF]
This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some
Søren Højsgaard, Jared O'Connell
core
Lubricant Oil Degradation, Condition Monitoring, and Service Life Estimation: A Review
Journal of the American Oil Chemists' Society, EarlyView.Research framework of the study, integrating lubricant oil degradation, condition monitoring, and service life estimation. ABSTRACT Lubricant oil is often changed on fixed time schedules, even though its real condition depends on load, temperature, and contamination, so good oil is discarded in some cases and degraded oil stays in service in others ...
Laith Shatnawi +5 more
wiley +1 more source
Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome
Genetics and Molecular Biology, 2003 Dependencies in DNA sequences are frequently modeled using Markov models. However, Markov chains cannot account for heterogeneity that may be present in different regions of the same DNA sequence.
Silva Cibele Q. da
doaj