Results 31 to 40 of about 24,804 (235)

A Spectral Algorithm for Inference in Hidden Semi-Markov Models

CoRR, 2014
Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs.
Igor Melnyk, Arindam Banerjee 0001
openaire   +4 more sources

A Sensor-Based Scheme for Activity Recognition in Smart Homes using Dempster-Shafer Theory of Evidence [PDF]

Journal of Artificial Intelligence and Data Mining, 2017
This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference ...
V. Ghasemi, A. Pouyan, M. Sharifi
doaj   +1 more source

Scale-Dependent Attraction of Invasive Raccoons to Bait Sites: Behavioural and Proximity Responses in a Post-Disaster Agricultural Landscape. [PDF]

Ecol Evol
Using cafeteria‐style bait trials and GPS telemetry, we investigated scale‐dependent responses of invasive raccoons to baiting in a post‐nuclear‐disaster agricultural landscape in Fukushima, Japan. Baiting induced strong short‐term and daily‐scale attraction to trap sites but did not restructure long‐term space use, highlighting the need to balance ...
Watanabe A, Nakamura D, Yoshida M, Hoshi N.   +3 more
europepmc   +2 more sources

Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM [PDF]

Journal of Artificial Intelligence and Data Mining, 2019
Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems ...
M. Asadolahzade Kermanshahi, M. M. Homayounpour   +1 more
doaj   +1 more source

Structured Inference for Recurrent Hidden Semi-markov Model [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into  Hidden Markov Models.
Hao Liu, Lirong He, Haoli Bai, Bo Dai 0001, Kun Bai, Zenglin Xu   +5 more
openaire   +1 more source

Perfect posterior simulation for mixture and hidden Markov models [PDF]

, 2010
In this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models.
Berthelsen, Kasper Klitgaard, Laird A. Breyer, Berthelsen, Kasper K., Kasper K. Berthelsen, Roberts, Gareth O., Breyer, Laird A., Gareth O. Roberts   +6 more
core   +1 more source

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Complexity, 2018
We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography.
Nebojša Malešević, Dimitrije Marković, Gunter Kanitz, Marco Controzzi, Christian Cipriani, Christian Antfolk   +5 more
doaj   +1 more source

biomvRhsmm:Genomic Segmentation with Hidden Semi-Markov Model [PDF]

BioMed Research International, 2014
High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular ...
Yang Du, Eduard Murani, Siriluck Ponsuksili, Klaus Wimmers   +3 more
openaire   +2 more sources

A statistical multiresolution approach for face recognition using structural hidden Markov models [PDF]

, 2007
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM).
A. Amira, P Nicholl, D. Bouchaffra, Perrott, R H, P. Nicholl, R H Perrott, Perrott, RH, D Bouchaffra, Nicholl, P, Amira, A, Bouchaffra, D, A Amira, R. H. Perrott   +12 more
core   +1 more source

Optimal Detection and Error Exponents for Hidden Semi-Markov Models [PDF]

IEEE Journal of Selected Topics in Signal Processing, 2018
We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals (HSMS), which generalize classical Markov chains by introducing explicit (possibly non-geometric ...
Dragana Bajovic, Kanghang He, Lina Stankovic, Dejan Vukobratovic, Vladimir Stankovic 0001   +4 more
openaire   +3 more sources