Filters








654,029 Hits in 10.7 sec

Network or regression-based methods for disease discrimination: a comparison study

Xiaoshuai Zhang, Zhongshang Yuan, Jiadong Ji, Hongkai Li, Fuzhong Xue
<span title="2016-08-18">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/llc2bilew5glxlrb6bkj3lko2a" style="color: black;">BMC Medical Research Methodology</a> </i> &nbsp;
For the special wheel network structure, logistic regression had a considerable performance compared to others.  ...  It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform.  ...  Availability of data and materials Additional data is available on request from the first author. Consent for publication Not applicable.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12874-016-0207-2">doi:10.1186/s12874-016-0207-2</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27538955">pmid:27538955</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4991108/">pmcid:PMC4991108</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t6owdjvxyjga7jfpivmppfro3a">fatcat:t6owdjvxyjga7jfpivmppfro3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171114121217/http://publisher-connector.core.ac.uk/resourcesync/data/Springer-OA/pdf/d62/aHR0cDovL2xpbmsuc3ByaW5nZXIuY29tLzEwLjExODYvczEyODc0LTAxNi0wMjA3LTIucGRm.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3c/b5/3cb545291a8fad4dc5b4e1562365e04e81475be2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12874-016-0207-2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991108" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

The Comparison of Generalized Additive Model with Artificial Hierarchical Neural Network in the Analysis of Pharmaceutical Data

Tatsuya Takagi, Emiko Kurokawa, Kouji Miyata, Kousuke Okamoto, Yuko Tanaka, Ken Kurokawa, Teruo Yasunaga
<span title="">2002</span> <i title="Division of Chemical Information and Computer Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/na7s6vrtjvdyzhomacc44iqmiy" style="color: black;">Journal of Computer Aided Chemistry</a> </i> &nbsp;
Although the Generalized Additive Model (GAM) is known as a superior nonparametric regression method, there have been only a few applications, especially in the fields of chemistry and pharmaceutical sciences  ...  In this study, GAM was compared with ANN in regression, classification, and prediction power using artificial and actual pharmaceutical data sets.  ...  Acknowledgement This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C), 13672253, 2001.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2751/jcac.3.56">doi:10.2751/jcac.3.56</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j6qw4lw2yjhypi64lru6n3b5lu">fatcat:j6qw4lw2yjhypi64lru6n3b5lu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030164716/https://www.jstage.jst.go.jp/article/jcac/3/0/3_0_56/_pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/43/1b/431babaa64b3bde85e906f8b75b39c48e4088809.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2751/jcac.3.56"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Neural Networks as Statistical Tools for Business Researchers

Kristen Bell Detienne, David H. Detienne, Shirish A. Joshi
<span title="">2003</span> <i title="SAGE Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dec5j7wemzg5tidmatcoyx2m7m" style="color: black;">Organizational Research Methods</a> </i> &nbsp;
Artificial neural networks are rapidly gaining popularity in the hard sciences and in social science. This article discusses neural networks as tools business researchers can use to analyze data.  ...  Then, the characteristics and organization of neural networks are presented, and the article shows why they are an attractive alternative to regression.  ...  The simplest method is to fit the model using only one part of the data and using the other part of the data to evaluate the model's performance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1177/1094428103251907">doi:10.1177/1094428103251907</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yqa34rq75jefdjkfi6uqgnw4bq">fatcat:yqa34rq75jefdjkfi6uqgnw4bq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320085214/http://leeds-faculty.colorado.edu/dahe7472/10%201%201%20136%208775.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/46/f6/46f65bf9283ce2e9a586a58b1702587a93ce1bd7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1177/1094428103251907"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> sagepub.com </button> </a>

Synthetic Q-Space Learning With Deep Regression Networks For Prostate Cancer Characterisation With Verdict

Vanya Valindria, Marco Palombo, Eleni Chiou, Saurabh Singh, Shonit Punwani, Eleftheria Panagiotaki
<span title="2021-04-13">2021</span> <i title="IEEE"> 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) </i> &nbsp;
Recently, q-space learning utilises machine learning methods to overcome these issues and to infer diffusion metrics.  ...  Our results show that while simple MLP is adequate to estimate parametric maps on simple models like classic VER-DICT, deep residual regression networks are needed for more complex models such as VERDICT  ...  R-VERDICT maps generated from deep regression models, compared to NLSS as the reference.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi48211.2021.9434096">doi:10.1109/isbi48211.2021.9434096</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lg2fklbux5ggrl76w2uapjlgxu">fatcat:lg2fklbux5ggrl76w2uapjlgxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715104148/https://discovery.ucl.ac.uk/id/eprint/10124649/1/ISBI_2021_Valindria_Final.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/11/f9/11f994d8800911d0832c8067407ecea3f2f87a61.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isbi48211.2021.9434096"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project

Hong-Gyu Cho, Kyong-Gon Kim, Jang-Young Kim, Gwang-Hee Kim
<span title="2013-02-20">2013</span> <i title="The Korean Institute of Building Construction"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/usatq3cm2bfv7enstigcjbgmia" style="color: black;">Journal of the Korea Institute of Building Construction</a> </i> &nbsp;
In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network  ...  A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model  ...  been studied [9, 10] In addition, there have been some studies in which the neural network was employed to improve the accuracy of prediction and overcome the restrictions of a regression analysis model  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5345/jkibc.2013.13.1.066">doi:10.5345/jkibc.2013.13.1.066</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5s5elifugrclli6xqiflojtski">fatcat:5s5elifugrclli6xqiflojtski</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706060624/http://ocean.kisti.re.kr/downfile/volume/kibc/GCSGBX/2013/v13n1/GCSGBX_2013_v13n1_66.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/a9/f6a9e8d055018e459231a9dd08029201fbcee221.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5345/jkibc.2013.13.1.066"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Pattern Based Multivariable Regression using Deep Learning (PBMR-DP) [article]

Jiztom Kavalakkatt Francis, Chandan Kumar, Jansel Herrera-Gerena, Kundan Kumar, Matthew J Darr
<span title="2022-03-09">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In addition to this data preparation methodology, we explore the use of state-of-the-art architectures to generate regression outputs to predict agricultural crop continuous yield information.  ...  We used a conversion of sensors-to-image which enables us to take advantage of Computer Vision architectures and training processes.  ...  We will use the same ideology to directly generate (in this particular case) a 3D data array in the range 0 and 1.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.13541v3">arXiv:2202.13541v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xbsy27xn2bejnmdjo6muwmtamm">fatcat:xbsy27xn2bejnmdjo6muwmtamm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220309211004/https://arxiv.org/pdf/2202.13541v2.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/d6/d4d61617a0e1e6216bcc689c3743430146ff5629.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.13541v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A comparison of conventional linear regression methods and neural networks for forecasting educational spending

Bruce D. Baker, Craig E. Richards
<span title="">1999</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/turrqmsldvfwtcvpdaspztj7ka" style="color: black;">Economics of Education Review</a> </i> &nbsp;
: (1) recurrent backpropagation; (2) Generalized Regression; and (3) Group Method of Data Handling.  ...  Regarding prediction accuracy, neural network results ranged from comparable to superior with respect to the NCES model.  ...  Two alternatives used in addition to backpropagation in this study are (1) Generalized Regression neural networks (GRNN) (Specht, 1991) and (2) Group Method of Data Handling (GMDH) polynomial neural  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0272-7757(99)00003-5">doi:10.1016/s0272-7757(99)00003-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3v2lhpkcufedfjwvab236trdz4">fatcat:3v2lhpkcufedfjwvab236trdz4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812162036/http://directory.umm.ac.id/Data%20Elmu/jurnal/E/Economics%20of%20Education%20Review/Vol18.Issue4.Oct1999/311.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ad/a3/ada3c100fdcdf9d23bc8a134bd17bcf1a030fbc0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0272-7757(99)00003-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Modelling of Bektas Creek Daily Streamflow with Generalized Regression Neural Network Method

Fatma AKCAKOCA, Halit APAYDIN
<span title="">2020</span> <i title="Sretechjournal Publication"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t7gkmtmosnchrobulekbrcvtfe" style="color: black;">International Journal of Advances in Scientific Research and Engineering</a> </i> &nbsp;
To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model.  ...  Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN).  ...  Generalized Regression Neural Network (GRNN) The generalized regression neural network consists of four layers: input, pattern, addition, and output layer (Figure 4 ).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31695/ijasre.2020.33717">doi:10.31695/ijasre.2020.33717</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6w34slhi2fctbmwoodscjdsdi4">fatcat:6w34slhi2fctbmwoodscjdsdi4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201105183948/https://ijasre.net/index.php/ijasre/article/download/452/672" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c2/21/c221fbbd7d82ac0a718eb064ee1b931337aefcac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31695/ijasre.2020.33717"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Predictive analysis of microbial water quality using machine-learning algorithms

Hadi Mohammed, Andreas Longva, Razak Seidu
<span title="2018-06-26">2018</span> <i title="Publishing House Technologija"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gwe6poa7trhc7euo3khlky6gc4" style="color: black;">Aplinkos tyrimai, inzinerija ir vadyba / Environmental Research, Engineering and Management</a> </i> &nbsp;
In addition, input data were subjected to different normalization methods to determine their effects on the performances of both ANN and SVM models.  ...  Both the ANN and regression SVM have comparable abilities in predicting the levels of the faecal indicator organisms in raw water.  ...  Acknowledgement The authors wish to show gratitude to the management of the Oset drinking water treatment plant in Oslo, Norway, for providing the water quality data used in this study.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5755/j01.erem.74.1.20083">doi:10.5755/j01.erem.74.1.20083</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/psg4vabpl5crnddmiwf4jaxvqe">fatcat:psg4vabpl5crnddmiwf4jaxvqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212233936/http://erem.ktu.lt/index.php/erem/article/download/20083/9450" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/4c/524cabc2575cc527dfeedceba64247eef444957b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5755/j01.erem.74.1.20083"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Comparison of Machine Learning Methods for Intelligent Tutoring Systems [chapter]

Wilhelmiina Hämäläinen, Mikko Vinni
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper, we tackle this problem, and give general outlines for creating accurate classifiers for educational data.  ...  To implement real intelligence or adaptivity, the models for intelligent tutoring systems should be learnt from data.  ...  They determine only, how to reason in the model, but the model itself is predefined. This situation is very surprising, compared to other fields, where machine learning methods are widely used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11774303_52">doi:10.1007/11774303_52</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vnjeg4hic5c3bfmwx4dwkzxjim">fatcat:vnjeg4hic5c3bfmwx4dwkzxjim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20070227123037/http://www.cs.joensuu.fi:80/~whamalai/articles/its.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/8a/5a8a6e5387c61a60097dae581c8842e84ad0b52b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11774303_52"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey

M Parsaeian, K Mohammad, M Mahmoudi, H Zeraati
<span title="2012-06-30">2012</span> <i title="Tehran University of Medical Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzzvxsynxnd23kp6db2ufin2f4" style="color: black;">Iranian Journal of Public Health</a> </i> &nbsp;
Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data.  ...  The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain.  ...  Acknowledgements The authors wish to acknowledge participants in the Second National Health for providing database, assistance, and technical support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23113198">pmid:23113198</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3469002/">pmcid:PMC3469002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nwfqu6udkzeylp4nt57cdyc5xi">fatcat:nwfqu6udkzeylp4nt57cdyc5xi</a> </span>
<a target="_blank" rel="noopener" href="https://archive.org/download/pubmed-PMC3469002/PMC3469002-ijph-41-86.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> File Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b2/2e/b22e624034d8d0a0db633baea4d74d8a0e101794.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3469002" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Scale-Space Kernels for Additive Modeling [chapter]

Chandan K. Reddy, Jin-Hyeong Park
<span title="">2008</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Similarities and distinctions of the proposed algorithm with the popularly used radial basis function networks and wavelet decomposition method are also discussed.  ...  Though demonstrated specifically in the context of boosting algorithms, our approach is generic enough to be accommodated in general additive modeling techniques.  ...  Relation to Other Models Our method appears to have close connection to popularly used Radial Basis Function (RBF) Networks [2] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-89689-0_75">doi:10.1007/978-3-540-89689-0_75</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hq4cvhhjx5a5tk6wvdssbjcfpa">fatcat:hq4cvhhjx5a5tk6wvdssbjcfpa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505011621/https://link.springer.com/content/pdf/10.1007%2F978-3-540-89689-0_75.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/93/ab9311799f71a0ff00cd9e576d65536f1adb401c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-89689-0_75"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

Taner Tunç
<span title="">2012</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
Logistic regression (LR) is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure.  ...  Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function.  ...  We compared the proposed method with logistic regression and artificial neural networks in terms of lung cancer data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2012/241690">doi:10.1155/2012/241690</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xn7k4k5qxffwhltg7y2ik2p43e">fatcat:xn7k4k5qxffwhltg7y2ik2p43e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426094713/http://downloads.hindawi.com/journals/mpe/2012/241690.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/63/f8/63f8cb8269d9d503b9152008471daf412d427f11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2012/241690"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Local linear regression for estimating time series data

Quinton J. Nottingham, Deborah F. Cook
<span title="">2001</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/akgkzs3bjzgpdixvnrfpwqkvki" style="color: black;">Computational Statistics &amp; Data Analysis</a> </i> &nbsp;
Local linear regression (LLR) is an additional nonparametric statistical method that can be used to estimate a time series response variable.  ...  The LLR procedure outperformed traditional time series techniques for the example stationary data sets and had comparable results to the ARIMA model for the example seasonal data set.  ...  Finally, results are generated using the LLR technique and the performance of the LLR technique is compared to that of traditional time series methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0167-9473(01)00006-8">doi:10.1016/s0167-9473(01)00006-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5fqfjygwljho7jpmzm5n76tfru">fatcat:5fqfjygwljho7jpmzm5n76tfru</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809124307/http://isiarticles.com/bundles/Article/pre/pdf/24134.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/e0/d3e04118d7b366a566476e2a38ac0a1e96f062b8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0167-9473(01)00006-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring [article]

Ellen Park, Jae Deok Kim, Nadege Aoki, Yumeng Melody Cao, Yamin Arefeen, Matthew Beveridge, David Nicholson, Iddo Drori
<span title="2021-10-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network.  ...  We explore the generalization of our estimators to test data outside our training distribution from both ESM and BGC-Argo data.  ...  Acknowledgements We acknowledge model output from IPSL via CMIP and float data from the International Argo Program and the national programs that contribute to it.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.00126v1">arXiv:2111.00126v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/42ysnd2zbndulhfa5vmu6r3rbi">fatcat:42ysnd2zbndulhfa5vmu6r3rbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211104112224/https://arxiv.org/pdf/2111.00126v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/b2/05b23dad69a8038d5d07875a17c4379bcb4d7c38.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.00126v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
&laquo; Previous Showing results 1 &mdash; 15 out of 654,029 results