Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Additive smoothing"'
Publikováno v:
Computers & Graphics. 98:1-10
In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f3ce9920539da049f97fd9f36b0902b
https://doi.org/10.1016/j.cag.2021.04.035
https://doi.org/10.1016/j.cag.2021.04.035
Autor:
Masaru Kanba, Kanta Naito
Publikováno v:
Journal of Data Science. 9:549-564
This paper discusses the selection of the smoothing parameter necessary to implement a penalized regression using a nonconcave penalty function. The proposed method can be derived from a Bayesian viewpoint, and the resultant smoothing parameter is gu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3dcd139a9e68d508e673d0764754bdf
https://doi.org/10.6339/jds.201110_09(4).0005
https://doi.org/10.6339/jds.201110_09(4).0005
Autor:
Liqiang Zhu
Publikováno v:
2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS).
This paper presented a kind of plant leaf classification method based on naive Bayesian classifier and leaf shape features. Firstly, estimate the class-conditional probability of the numeric features with the distribution function of Gaussian distrib
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b23af8fd1519915442f560f2b0a5f86
https://doi.org/10.1109/icsess52187.2021.9522331
https://doi.org/10.1109/icsess52187.2021.9522331
Publikováno v:
2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT).
Being famous for a classification algorithm using a simple statistic calculation, Naive Bayes produces a relatively low accuracy. This research tests how combining the Naive Bayes classifier using Chi-Square as its feature selection, accompanied by L
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a12851f536eab8aa43fded208bfdf04d
https://doi.org/10.1109/eiconcit50028.2021.9431862
https://doi.org/10.1109/eiconcit50028.2021.9431862
Autor:
F. Nurhadiansyah, G. Ramantoko
Publikováno v:
Synergizing Management, Technology and Innovation in Generating Sustainable and Competitive Business Growth ISBN: 9781003138914
Synergizing Management, Technology and Innovation in Generating Sustainable and Competitive Business Growth
Synergizing Management, Technology and Innovation in Generating Sustainable and Competitive Business Growth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d04d4639270d7861d79f7712bcb15f23
https://doi.org/10.1201/9781003138914-46
https://doi.org/10.1201/9781003138914-46
Publikováno v:
Journal of Statistical Theory and Practice. 15
The estimation of the minimum probability of a multinomial distribution is important for a variety of application areas. However, standard estimators such as the maximum likelihood estimator and the Laplace smoothing estimator fail to function reason
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90134f8eaea6137f51e8507ce8897821
https://doi.org/10.1007/s42519-020-00163-y
https://doi.org/10.1007/s42519-020-00163-y
Publikováno v:
Modeling Decisions for Artificial Intelligence ISBN: 9783030855284
MDAI
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Modeling Decisions for Artificial Intelligence
MDAI
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Modeling Decisions for Artificial Intelligence
Regression problems have been widely studied in machine learning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate catego
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::350d900e0f5910a0503c474cdaf7d796
https://doi.org/10.1007/978-3-030-85529-1_14
https://doi.org/10.1007/978-3-030-85529-1_14
Publikováno v:
2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).
Due to the development of big data technology, traditional machine learning algorithms are difficult to deal with massive data. To solve this problem, a naive Bayesian classifier based on parallel training and prediction on Spark platform is proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d7b44211d8fb83b1170468048e16f54
https://doi.org/10.1109/iccwamtip51612.2020.9317362
https://doi.org/10.1109/iccwamtip51612.2020.9317362
Publikováno v:
KDD 2020-26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KDD 2020-26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2020, San Diego / Virtual, United States. pp.1254--1264
KDD
KDD 2020-26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 2020, San Diego / Virtual, United States. pp.1254--1264
KDD
International audience; We consider the task of discovering the top-K reliable approximate functional dependencies X → Y from high dimensional data. While naively maximizing mutual information involving high dimensional entropies over empirical dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::355a22f0e573cbb920d08485eeb62821
https://hal-centralesupelec.archives-ouvertes.fr/hal-02880553/file/smoothed-info-article-without-ACM.pdf
https://hal-centralesupelec.archives-ouvertes.fr/hal-02880553/file/smoothed-info-article-without-ACM.pdf
Publikováno v:
Computer Aided Verification ISBN: 9783030532901
CAV (2)
Computer Aided Verification
CAV 2020-32nd International Conference on Computer-Aided Verification
CAV 2020-32nd International Conference on Computer-Aided Verification, Jul 2020, Los Angeles, United States. pp.304-326
CAV (2)
Computer Aided Verification
CAV 2020-32nd International Conference on Computer-Aided Verification
CAV 2020-32nd International Conference on Computer-Aided Verification, Jul 2020, Los Angeles, United States. pp.304-326
International audience; Learning models from observations of a system is a powerful tool with many applications. In this paper, we consider learning Discrete Time Markov Chains (DTMC), with different methods such as frequency estimation or Laplace sm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09d8a62e8c0737b174dc1186e0a68797
https://doi.org/10.1007/978-3-030-53291-8_17
https://doi.org/10.1007/978-3-030-53291-8_17