Results 31 to 40 of about 81,222 (134)

A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition [PDF]

, 2016
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
Bellet, Aurelien, Collins, Michael, Fan, Linxi, Garakani, Alireza Bagheri, Guo, Dong, Kingsbury, Brian, Liu, Kuan, Lu, Zhiyun, May, Avner, Picheny, Michael, Sha, Fei   +10 more
core   +4 more sources

An Evidential Software Risk Evaluation Model

Mathematics, 2022
Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area.
Xingyuan Chen, Yong Deng
doaj   +1 more source

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling [PDF]

, 2019
A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP).
Akhmedova-Azizova, Arcoumanis, Arikol, Ashutosh Gupta, Assael, Assael, Assael, Assael, Assael, Baroncini, Baroncini, Baroncini, Baroncini, Battistoni, Briggs, Burgess, Chickos, Chung, Chung, Deng, Fareleira, Fareleira, Fouad, Gharagheizi, Gharagheizi, Gharagheizi, Giannadakis, Gross, Guo, Hopp, Houman B. Rokni, Huber, Huber, Huber, Jia, Joshua D. Moore, Kashiwagi, Kesler, Lashkarbolooki, Latini, Latini, Lazzús, Liang, Lötgering-Lin, Manolis Gavaises, Mark A. McHugh, Mathias, Nagvekar, Park, Pomraning, Rajendar R. Mallepally, Ramos-Pallares, Ramos-Pallares, Rastorguev, Rodenbush, Rokni, Rokni, Rosenfeld, Roy, Sastri, Sauer, Schilling, Schmidt, Stiel, Tamouza, Teja, Teja, Thi, Tihic, Wada, Wakeham, Wu, Xu   +72 more
core   +1 more source

A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function

Entropy, 2018
How to measure the uncertainty of the basic probability assignment (BPA) function is an open issue in Dempster⁻Shafer (D⁻S) theory.
Lipeng Pan, Yong Deng
doaj   +1 more source

Conflict Management of Evidence Theory Based on Belief Entropy and Negation

IEEE Access, 2020
Discount coefficient is an efficient method to address conflicting evidence combination in Dempster-Shafer evidence theory. However, how to determine the discount coefficient of each evidence is an open issue.
Shanshan Li, Fuyuan Xiao, Jemal H. Abawajy   +2 more
doaj   +1 more source

Universal statistics of the knockout tournament [PDF]

, 2013
We study statistics of the knockout tournament, where only the winner of a fixture progresses to the next. We assign a real number called competitiveness to each contestant and find that the resulting distribution of prize money follows a power law with ...
Baek, Seung Ki, Kim, Beom Jun, Park, Hye Jin, Yi, Il Gu   +3 more
core   +2 more sources

Evidential Decision Tree Based on Belief Entropy

Entropy, 2019
Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute ...
Mujin Li, Honghui Xu, Yong Deng
doaj   +1 more source

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Entropy, 2021
Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results.
Fanghui Huang, Yu Zhang, Ziqing Wang, Xinyang Deng   +3 more
doaj   +1 more source

Information Entropies for H2 and ScF Diatomic Molecules with Deng- Fan-Eckart Potential

Revista Mexicana de Física, 2020
In this study, the Shannon entropy and the Fisher information is investigated with molecular Deng-Fan-Eckart potential for the diatomic molecules  and ScF in position and momentum spaces in three dimensions for the ground and the excited states. The results were numerically obtained for diatomic molecules.
P. O. Amadi, A. N. Ikot, U. S. Okorie, G. J. Rampho, Hewa Y Abdullah, B. C. Lütfüoğlu   +5 more
openaire   +1 more source

Optimal Kullback-Leibler Aggregation via Information Bottleneck

, 2015
In this paper, we present a method for reducing a regular, discrete-time Markov chain (DTMC) to another DTMC with a given, typically much smaller number of states.
Geiger, Bernhard C., Koeppl, Heinz, Kubin, Gernot, Petrov, Tatjana   +3 more
core   +1 more source