Results 31 to 40 of about 437,723 (274)
An Introduction to Conditional Random Fields [PDF]
Foundations and Trends® in Machine Learning, 2012 Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform ...
Sutton, Charles, McCallum, Andrew
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Bearing Fault Classification Based on Conditional Random Field
Shock and Vibration, 2013 Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment.
Guofeng Wang, Xiaoliang Feng, Chang Liu
doaj +1 more source
Source-Device-Independent Ultrafast Quantum Random Number Generation [PDF]
, 2017 Secure random numbers are a fundamental element of many applications in science, statistics, cryptography and more in general in security protocols.
MARANGON, DAVIDE GIACOMO +2 more
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Research of ddi based on multi-label conditional random field
BIO Web of Conferences, 2017 The detection of drug name and drug-drug interaction(DDI) is considered as a sequence labeling task in this paper. We present the multi-label CRF method to complete it.
Yu Yangzhi, Deng Hongtao, Zhu Xun
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Linear street extraction using a Conditional Random Field model [PDF]
, 2015 A novel method for extracting linear streets from a street network is proposed where a linear street is defined as a sequence of connected street segments having a shape similar to a straight line segment.
Bertolotto, Michela +2 more
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MULTI-SOURCE MULTI-SCALE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR REMOTE SENSING IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Fusion of remote sensing images and LiDAR data provides complimentary information for the remote sensing applications, such as object classification and recognition.
Z. Zhang, M. Y. Yang, M. Zhou
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, 2014 In this paper, we consider the extreme behavior of a Gaussian random field $f(t)$ living on a compact set $T$. In particular, we are interested in tail events associated with the integral $\int_Te^{f(t)}\,dt$.
Liu, Jingchen, Xu, Gongjun
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Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework
IEEE Access, 2015 The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is gaining attraction for the autodetection of prostate tumors, since it provides a plethora of mineable data, which can be used ...
Audrey G. Chung +4 more
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Chinese Sequence Labeling Based on Stack Pre-training Model
Journal of Harbin University of Science and Technology, 2022 Sequence labeling is an important task in natural language processing. In this paper, according to the relevance of tasks, we use stacking pretraining model to extract features, segment words, and name entity recognition/chunk tagging.Through in-depth ...
LIU Yu-peng, LI Guo-dong
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Quantifying uncertainties on excursion sets under a Gaussian random field prior [PDF]
, 2016 We focus on the problem of estimating and quantifying uncertainties on the excursion set of a function under a limited evaluation budget. We adopt a Bayesian approach where the objective function is assumed to be a realization of a Gaussian random field.
Azzimonti, Dario +3 more
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