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Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection

Huaming Chen, Hong Zhao, Jun Shen, Rui Zhou, Qingguo Zhou
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/huj3ntmitbe2bjrhmeqw7skjji" style="color: black;">2015 IEEE International Congress on Big Data</a> </i> &nbsp;
In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better  ...  In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better  ...  ) deployed in [1] and ensemble-based feature selection methods combined with the incremental feature selection(IFS) stratege in [10] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdatacongress.2015.28">doi:10.1109/bigdatacongress.2015.28</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bigdata/ChenZS0Z15.html">dblp:conf/bigdata/ChenZS0Z15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x7bwoqgnsnba7k3pn52rb6ska4">fatcat:x7bwoqgnsnba7k3pn52rb6ska4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922025252/http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6043&amp;context=eispapers" 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/26/58/265825ef7746a7ad7764b32833befa8f258e8540.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdatacongress.2015.28"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Selective background Monte Carlo simulation at Belle II

James Kahn, Thomas Kuhr, Andreas Maximilian Lindner
<span title="2019-11-05">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
To investigate such rare processes in a high data volume environment necessitates a correspondingly high volume of Monte Carlo simulations to prepare analyses and gain a deep understanding of the contributing  ...  The large volume of data expected to be produced by the Belle II experiment presents the opportunity for for studies of rare, previously inaccessible processes.  ...  Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) Selective background Monte Carlo simulation at Belle II -James Kahn, Andreas Lindner, Thomas Kuhr c a 1  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3599471">doi:10.5281/zenodo.3599471</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7pkbetxoy5bfxfm6pvgbk7w4tm">fatcat:7pkbetxoy5bfxfm6pvgbk7w4tm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200211213650/https://zenodo.org/record/3599471/files/CHEP2019_441.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/9e/3a/9e3a618ac8cc334acf1f258750a64ec3c8ab5cb0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3599471"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Yuantao Chen, Jiajun Tao, Jin Wang, Xi Chen, Jingbo Xie, Jie Xiong, Kai Yang
<span title="2019-07-17">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp; <span class="release-stage" style="color: red;">retracted</span>
To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing  ...  Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features.  ...  Acknowledgments: We are grateful to our anonymous referees for their useful comments and suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19143145">doi:10.3390/s19143145</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31319556">pmid:31319556</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6679324/">pmcid:PMC6679324</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2cqnoogmx5gl5kgepmxthkd5m4">fatcat:2cqnoogmx5gl5kgepmxthkd5m4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200225232203/https://pdfs.semanticscholar.org/b18f/fbcdd25e13bf3c8f9beab183849c9b441e0a.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/b1/8f/b18ffbcdd25e13bf3c8f9beab183849c9b441e0a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19143145"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679324" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models [article]

Lei Li, Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun
<span title="2021-12-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The achieve this, we design a Model Uncertainty--aware Knowledge Amalgamation~(MUKA) framework, which identifies the potential adequate teacher using Monte-Carlo Dropout for approximating the golden supervision  ...  In this paper, we explore a novel model reuse paradigm, Knowledge Amalgamation~(KA) for PLMs.  ...  Method AG News THUCNews MUKA-Hard 87.0 ± 0.40 97.2 ± 0.12 w/o Monte-Carlo Dropout 65.1 ± 1.67 97.0 ± 0.13 w/o Instance Re-weighting 85.5 ± 0.51 96.9 ± 0.06 MUKA-Soft 87.1 ± 0.19 97.2 ± 0.08 w/o Monte-Carlo  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07327v1">arXiv:2112.07327v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e2metiycsrbrdlqitfzth2ekqe">fatcat:e2metiycsrbrdlqitfzth2ekqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211216213823/https://arxiv.org/pdf/2112.07327v1.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/7d/c9/7dc93d32613e8277eca1fdd8f414703f8969c132.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07327v1" 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>

Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods [chapter]

Francis Maes, Pierre Geurts, Louis Wehenkel
<span title="">2012</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;
It is based on the formalization of feature construction as a sequential decision making problem addressed by a tractable Monte Carlo search algorithm coupled with node splitting.  ...  Feature generation is the problem of automatically constructing good features for a given target learning problem.  ...  Monte Carlo search for feature generation Monte Carlo search algorithms for making optimal decisions are receiving an increasing interest in various fields of artificial intelligence [4] , essentially  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-33460-3_18">doi:10.1007/978-3-642-33460-3_18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mfwv4fivzncazmh2f4es37jgmi">fatcat:mfwv4fivzncazmh2f4es37jgmi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130725122857/http://www.cs.bris.ac.uk/~flach/ECMLPKDD2012papers/1125530.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/96/8f/968f35c1d5782b10d73cef9e78c31a761112c769.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-33460-3_18"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Xin Wang, Sicong Liu, Peijun Du, Hao Liang, Junshi Xia, Yunfeng Li
<span title="2018-02-11">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed. [8, 9] .  ...  classification.  ...  Figure 8 shows the EL results in one Monte Carlo run for different scales and feature selection strategies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs10020276">doi:10.3390/rs10020276</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6744rt4bnnbnxbdfgu5hvo4j6e">fatcat:6744rt4bnnbnxbdfgu5hvo4j6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180725001416/https://res.mdpi.com/def502008fedbb3e9eab287232e8d3aa53b03649096a04a7c032c9869014a0d29773350e0a2630f779baad7f9abc2be5d3a33e867f0a3c5d5c888ec17ef9c8baad1b8587fe7e2d4901bf626115fc17af54b4c277c1dc6b282fb569a4c1212525d9fc64213d290d49a2062ff562b00d08f2ea7c82488b4dc1cd45b9277af086989ccc8e56ff4e9a27c7e5ab2e50b1069fcb2c18224cde5be97577ee1cda60c2afe6c0?filename=&amp;attachment=1" 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/b4/ef/b4ef00e7d5514c033535ef1376cd906ef5bb408d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs10020276"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

AlphaGO – An AI in gaming

<span title="2017-06-07">2017</span> <i title="International Journal of Recent Trends in Engineering and Research"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q6j7kxpeujbvdfdbleu52jna4a" style="color: black;">International Journal of Recent Trends in Engineering and Research</a> </i> &nbsp;
Gaming industries are looking for new ways to improve the gaming experience for the consumer.  ...  Today with the gaming industry evolving at very high rate and making a huge place for itself in the market.  ...  Monte Carlo tree search (MCTS) In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in game play. • Selection  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23883/ijrter.2017.3272.xwdqs">doi:10.23883/ijrter.2017.3272.xwdqs</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jie7qz52kbfc3ebyh2szgejauq">fatcat:jie7qz52kbfc3ebyh2szgejauq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180603073827/http://www.ijrter.com/papers/volume-3/issue-5/alphago-an-ai-in-gaming.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/89/b6/89b6f217a8f7b2217c5f543cc4506c2723a86590.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23883/ijrter.2017.3272.xwdqs"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Classification of cirrhotic liver in Gadolinium-enhanced MR images

Gobert Lee, Yoshikazu Uchiyama, Xuejun Zhang, Masayuki Kanematsu, Xiangrong Zhou, Takeshi Hara, Hiroki Kato, Hiroshi Kondo, Hiroshi Fujita, Hiroaki Hoshi, Maryellen L. Giger, Nico Karssemeijer
<span title="2007-03-08">2007</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xfwg4fmybzazfktmdtzvhcujka" style="color: black;">Medical Imaging 2007: Computer-Aided Diagnosis</a> </i> &nbsp;
A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers.  ...  Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regionsof-interest (ROIs).  ...  ACKNOWLEDGMENTS This research was supported in part by a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan and a Grant-in-Aid for Cancer  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.710288">doi:10.1117/12.710288</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/micad/LeeUZKZHKK0H07.html">dblp:conf/micad/LeeUZKZHKK0H07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7qs55lymwjeufnpp7qtoyudtaa">fatcat:7qs55lymwjeufnpp7qtoyudtaa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811090631/http://www.fjt.info.gifu-u.ac.jp/publication/500.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/9c/d0/9cd00e7349807cefacae4810c84347aa023c289b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.710288"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Data-driven Random Fourier Features using Stein Effect [article]

Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabas Poczos
<span title="2017-05-23">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte  ...  Carlo (QMC) integration [Yang et al., 2014].  ...  Acknowledgments We thank the reviewers for their helpful comments. This work is supported in part by the National Science Foundation (NSF) under grants IIS-1546329 and IIS-1563887.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1705.08525v1">arXiv:1705.08525v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iorvgfbsnvbl5dnozobcqrt6yi">fatcat:iorvgfbsnvbl5dnozobcqrt6yi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825052124/https://arxiv.org/pdf/1705.08525v1.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/a1/f6a16ba2b1099845ced323c5ebf5729401cff35e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1705.08525v1" 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>

Editorial

N. Bourbakis, Jeffrey Tsai, N. Filipovic
<span title="2021-02-18">2021</span> <i title="European Alliance for Innovation n.o."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/woryaympyzg4xbtvsolajdd6km" style="color: black;">EAI Endorsed Transactions on Bioengineering and Bioinformatics</a> </i> &nbsp;
bioinformatics, modelling methods, supervised classification, clustering and probabilistic graphical models for knowledge discovery, deterministic and stochastic heuristics for optimization and applications  ...  Carlo framework.  ...  bioinformatics, modelling methods, supervised classification, clustering and probabilistic graphical models for knowledge discovery, deterministic and stochastic heuristics for optimization and applications  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.18-2-2021.168722">doi:10.4108/eai.18-2-2021.168722</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jucsgr4zkrhdnoiaima6rdp5zm">fatcat:jucsgr4zkrhdnoiaima6rdp5zm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210501063503/https://eudl.eu/pdf/10.4108/eai.18-2-2021.168722" 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/9b/4d/9b4daada93c3ed91cd1c37d649dad28cfc857523.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.18-2-2021.168722"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Monte Carlo Schemata Searching for Physical Activity Recognition

Alejandro Baldominos, Pedro Isasi, Yago Saez, Bernard Manderick
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sm7hbtsg3rfqxlevks5londugi" style="color: black;">2015 International Conference on Intelligent Networking and Collaborative Systems</a> </i> &nbsp;
This paper proposes a novel approach for performing activity recognition using Monte Carlo Schemata Search (MCSS) for feature selection and random forests for classification.  ...  The experiments are conducted using leave-onesubject-out cross validation and attain classification accuracies of over 93% by using roughly one third of the total set of features.  ...  To improve the results feature selection will be carried out by means of a new method based on Monte Carlo Tree Search called Monte Carlo Schemata Search (MCSS).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/incos.2015.24">doi:10.1109/incos.2015.24</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/incos/BaldominosISM15.html">dblp:conf/incos/BaldominosISM15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qvo7tfwzhrg55ne4bjxgewahfa">fatcat:qvo7tfwzhrg55ne4bjxgewahfa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720231606/https://e-archivo.uc3m.es/bitstream/handle/10016/25710/monte_INCoS_2015_ps.pdf;jsessionid=A55B9851A12389B5A492DE357EB8970A?sequence=1" 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/ca/9c/ca9c04912c43917b5a6039f4cdd36fe549a722d1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/incos.2015.24"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Learning an un-Supervised – Clustering Algorithm Monte Carlo over Consensus Clustering for Genomic Data for Tumor Identification

<span title="2019-11-30">2019</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3sfifsouvjgadp4gfj54u3z2ku" style="color: black;">International journal of recent technology and engineering</a> </i> &nbsp;
Subgroup classification is a basic task in high-throughput genomic data analysis, especially for gene expression and methylation data analysis.  ...  Monte Carlo3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overfitting and can reject the null hypothesis when only one cluster is there.  ...  The Monte Carlo simulations maintains the feature correlation structure of the input data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.d7370.118419">doi:10.35940/ijrte.d7370.118419</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/unvmsfxyibcdvadywkwhrszljq">fatcat:unvmsfxyibcdvadywkwhrszljq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191218005225/https://www.ijrte.org/wp-content/uploads/papers/v8i4/D7370118419.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/dc/bd/dcbd8a9426310a52ae004614609afb14b7a787fe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.d7370.118419"> <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>

Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model

Jun Li, Jose M. Bioucas-Dias, Antonio Plaza
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ao54s6nulvbsdnbbncuz4yl4gi" style="color: black;">2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)</a> </i> &nbsp;
In this work, we propose a new semi-supervised classification algorithm for remotely sensed hyperspectral images.  ...  The proposed semi-supervised algorithm represents an innovative contribution with regards to conventional semi-supervised learning algorithms that only assign hard labels to unlabeled samples.  ...  Table 1 . 1 OA [%] OA [%], κ [%] and AA [%] results for 10 Monte Carlo (MC) runs for the proposed semi-supervised algorithm with 0 and 1577 unlabeled samples, respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/whispers.2011.6080879">doi:10.1109/whispers.2011.6080879</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/whispers/LiBP11.html">dblp:conf/whispers/LiBP11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pw7xyuyhdfcb3fp6tylhxjm7ha">fatcat:pw7xyuyhdfcb3fp6tylhxjm7ha</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120130210814/http://www.umbc.edu/rssipl/people/aplaza/Papers/Conferences/2011.WHISPERS.Soft.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/20/5a20c354e166c34f3944dd505af32cb308c3a130.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/whispers.2011.6080879"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Machine learning in bioinformatics

Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José A. Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez, Victor Robles
<span title="2006-03-01">2006</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/op7ztx4fhvairowgqifu7dnvsi" style="color: black;">Briefings in Bioinformatics</a> </i> &nbsp;
Supervised classification, clustering and probabilistic graphical models for bioinformatics are reviewed.  ...  It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization  ...  Acknowledgements The authors are grateful to the anonymous reviewers for their comments, which have helped us to greatly improve this article.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bbk007">doi:10.1093/bib/bbk007</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/16761367">pmid:16761367</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4oss26occvhkjnetcr3sesnkcu">fatcat:4oss26occvhkjnetcr3sesnkcu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809093100/http://www.ics.uci.edu/~xhx/courses/CS174/lectures/ML_in_Bioinform.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/51/4e/514eeeaee9fce0ebe4b218a0d5ffc371b4e40521.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bbk007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks [article]

Aneesh Komanduri, Justin Zhan
<span title="2021-12-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Introducing the Bayesian paradigm to graph-based models, specifically for semi-supervised node classification, has been shown to yield higher classification accuracies.  ...  In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS), which uses a Markov Chain Monte Carlo (MCMC) based graph sampling  ...  The GCN weights for Bayesian inference can be obtained by performing a variety of techniques such as expectation propagation [25] , variational inference [21] [26] [27] , and Markov chain Monte Carlo  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07743v1">arXiv:2112.07743v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jy5jjzyywjfcvjpqmhkwhxls2i">fatcat:jy5jjzyywjfcvjpqmhkwhxls2i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211217061740/https://arxiv.org/pdf/2112.07743v1.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/fc/43/fc4328a0d1c05b7f0bacdd1ff6eec694dbc01175.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.07743v1" 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>
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