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Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification [article]

Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura, Ryoma Bise
<span title="2021-07-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Then we select reliable pseudo labels from unlabeled data using recent machine learning techniques; positive-and-unlabeled (PU) learning and P-classification.  ...  We treat partially labeled cells as positive samples and the detected positions except for the labeled cell as unlabeled samples.  ...  based method for Unlabeled Add selected patches as Pseudo-labels Partially labeled data + Pseudo-labels Train with Patch images Positive Unlabeled + PU-learning Predicted Positive  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09289v2">arXiv:2107.09289v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qo5ehnksyrhvfotr3eqnksfueu">fatcat:qo5ehnksyrhvfotr3eqnksfueu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210723013249/https://arxiv.org/pdf/2107.09289v2.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/2f/39/2f39a70a71e905e75ea60a1917fa72e212a82020.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09289v2" 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>

Identifying TF-MiRNA Regulatory Relationships Using Multiple Features

Mingyu Shao, Yanni Sun, Shuigeng Zhou, Paolo Provero
<span title="2015-04-29">2015</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem.  ...  Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for  ...  It is possible that some false TFBS will be included. Thus we employ multiple features (described below) to control the false positive rate.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0125156">doi:10.1371/journal.pone.0125156</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25922940">pmid:25922940</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4414601/">pmcid:PMC4414601</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dyieaxjc45ar7pi46leaul7i2e">fatcat:dyieaxjc45ar7pi46leaul7i2e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171010200406/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0125156&amp;type=printable" 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/19/e2/19e2c216311b1ed7c0b7a53b8ec5afdd2f300ed5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0125156"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4414601" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach [chapter]

Yan Gao, Ming Yang, Alok Choudhary
<span title="">2009</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;
Experimental results demonstrate that our approach achieves 91.66% high detection rate with less than 2.96% false positive rate, meanwhile it significantly reduces the labeling cost.  ...  It makes the cost too high for fully supervised learning to frequently collect sufficient labeled data for training.  ...  ., it achieves both higher true positive rate and lower false positive rate than the DEM.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-03348-3_17">doi:10.1007/978-3-642-03348-3_17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x2xizupfa5gbhllzwnhev3b3je">fatcat:x2xizupfa5gbhllzwnhev3b3je</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809105408/http://users.eecs.northwestern.edu/~mya671/mypapers/ADMA09_Gao_Yang_Choudhary.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/86/01/8601944a013f43cef831ea49e594d45274982026.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-03348-3_17"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Be Sensitive to Your Errors

Guanhua Yan
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rau5643b7ncwvh74y6p64hntle" style="color: black;">Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security - ASIA CCS &#39;15</a> </i> &nbsp;
, i.e., false positive errors and false negative errors.  ...  This framework enforces the Neyman-Pearson criterion, which aims to maximize the detection rate under the constraint that the false positive rate should be no greater than a certain threshold.  ...  Acknowledgment We thank anonymous reviewers for their comments and our paper shepherd, Aziz Mohaisen, for his great help on improving the final version of this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2714576.2714578">doi:10.1145/2714576.2714578</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ccs/Yan15.html">dblp:conf/ccs/Yan15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tbnmhw5shjh7dhyapxrixyl5y4">fatcat:tbnmhw5shjh7dhyapxrixyl5y4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706052026/http://www.cs.binghamton.edu/%7Eghyan/papers/asiaccs15.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/fd/8f/fd8f930e1e48d68d680a8861dbef839ee981d39c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2714576.2714578"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Fairness Constraints in Semi-supervised Learning [article]

Tao Zhang, Tianqing Zhu, Mengde Han, Jing Li, Wanlei Zhou, Philip S. Yu
<span title="2020-09-14">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Hence, we develop a framework for fair semi-supervised learning, which is formulated as an optimization problem.  ...  Yet, in reality, most machine learning tasks rely on large datasets that contain both labeled and unlabeled data. One of key issues with fair learning is the balance between fairness and accuracy.  ...  positive rate and false negative rate.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.06190v1">arXiv:2009.06190v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/osapb3xowfarzp6thujc7cu7zy">fatcat:osapb3xowfarzp6thujc7cu7zy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200921214636/https://arxiv.org/pdf/2009.06190v1.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/98/45/9845d8cac2e67cd64dd4fcf907f259ded8b1f618.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.06190v1" 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>

Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification

Mingkuan Liu, Musen Wen, Selcuk Kopru, Xianjing Liu, Alan Lu
<span title="">2018</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kfvcf5eqhzf4pmhalvdhsperiq" style="color: black;">Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP</a> </i> &nbsp;
To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models.  ...  The experimental results show that the proposed method reduces the classification error rate by 4% and up to 15% across various experimental setups and datasets.  ...  Combining the advantages of the GAN framework and the proposed approach is a very interesting research direction for us in the future.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w18-3409">doi:10.18653/v1/w18-3409</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl-deeplo/LiuWKLL18.html">dblp:conf/acl-deeplo/LiuWKLL18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i6rgmarwpba3dmairlc4cekuvm">fatcat:i6rgmarwpba3dmairlc4cekuvm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200430141311/https://www.aclweb.org/anthology/W18-3409.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/2d/70/2d7044a4afd38b5506f3b416f3a080d735104072.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w18-3409"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data [chapter]

Konstantinos Sechidis, Gavin Brown
<span title="">2015</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The result is a significantly deeper understanding of how to control false negative errors in Markov Blanket discovery procedures and how unlabelled data can help.  ...  Our work derives a generalization of the conditional tests of independence for partially labelled binary target variables, which can handle the two main partially labelled scenarios: positive-unlabelled  ...  Since our techniques are informative in terms of power, they can be used in structure learning approaches that have control over the false negative rate to prevent over constraint structures; for example  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-23528-8_22">doi:10.1007/978-3-319-23528-8_22</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mhnwpm37mzfcljq5k2ecagq3g4">fatcat:mhnwpm37mzfcljq5k2ecagq3g4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151017125521/http://www.cs.man.ac.uk/~gbrown/publications/sechidis2015ecml.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/da/f6/daf600477cbe48d857a5c23e9201cc0841470241.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-23528-8_22"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

On Text-based Mining with Active Learning and Background Knowledge Using SVM

Catarina Silva, Bernardete Ribeiro
<span title="2006-04-20">2006</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/e5brmbb4nne2bb6o5whxervgii" style="color: black;">Soft Computing - A Fusion of Foundations, Methodologies and Applications</a> </i> &nbsp;
We further evaluate the possibility of actively learning and propose a method for successfully combining background knowledge and active learning.  ...  This paper addresses these problems by introducing information from unlabeled documents in the training set, using the support vector machine (SVM) separating margin as the differentiating factor.  ...  Acknowledgements CISUC -Center of Informatics and Systems of University of Coimbra and Project POSI/SRI/41234/2001 are gratefully acknowledged for partial financing support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00500-006-0080-8">doi:10.1007/s00500-006-0080-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4o23dx2xzvefjjlh7okbkfdpme">fatcat:4o23dx2xzvefjjlh7okbkfdpme</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922042235/https://estudogeral.sib.uc.pt/jspui/bitstream/10316/7640/1/obra.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/ea/46/ea46c116f46f16f511f5a01817fd5061d7fddb5b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00500-006-0080-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

PP-041 Computational prediction of type III secreted proteins using labeled and unlabeled data

Y. Yang
<span title="">2010</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6v6xxnaax5ay7dkx6nblo7l5l4" style="color: black;">International Journal of Infectious Diseases</a> </i> &nbsp;
This data set was divided into five subsets, four of which for training and the left one for test. In addition, 3000 unlabeled data were used in the semi-supervised learning.  ...  Poster Presentations S37 Result: Out of 267 samples, 133 gave positive results for B. abortus by real-time PCR.  ...  Under antituberculosis agents treatment GST activity extended to rise, but GSH level increased to positive control rate.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1201-9712(10)60109-9">doi:10.1016/s1201-9712(10)60109-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6s46smfzazfqzn4y7vb434b2ca">fatcat:6s46smfzazfqzn4y7vb434b2ca</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190314072935/https://core.ac.uk/download/pdf/82521903.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/52/d0/52d05d3b923de221b4a2862f07f8fd1b766a6567.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1201-9712(10)60109-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion [article]

Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
<span title="2022-05-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To this end, we propose positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC.  ...  In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue.  ...  Positive-Unlabeled Learning for KGC Motivated by the aforementioned false negative issue, we aim to design a learning strategy to circumvent the impact of false negative samples.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.00904v2">arXiv:2205.00904v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5w6maabeazcdhagxio24oq3cdm">fatcat:5w6maabeazcdhagxio24oq3cdm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505110246/https://arxiv.org/pdf/2205.00904v1.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/72/15/7215154f31294feaf1991df5eaa1960790f46efd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.00904v2" 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>

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy

Elham Sherafat, Jordan Force, Ion I. Măndoiu
<span title="2020-12-30">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning.  ...  Results In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers.  ...  Srivastava and the members of his group at UConn Health's Carole and Ray Neag Comprehensive Cancer Center for helpful discussions and suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-020-03813-x">doi:10.1186/s12859-020-03813-x</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33375939">pmid:33375939</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6d6p36vr6fa4tpg3rbajh3jdfq">fatcat:6d6p36vr6fa4tpg3rbajh3jdfq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210101130944/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03813-x.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/10/ca/10cacf6c182cd4ababe2f3b0dbd1bc98a8ab0821.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-020-03813-x"> <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>

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering [article]

Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin
<span title="2020-09-07">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we first provide a novel understanding of negative instances by empirically observing that only a few instances are potentially important for model learning, and false negatives tend to  ...  Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.  ...  A Comparison Between Different Approaches A.1 General Machine Learning Approaches Learning an implicit CF model from the positive-only data is also related to Positive-Unlabeled (PU) learning and learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.03376v1">arXiv:2009.03376v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z4zndvivfbafndce2xasxyg6s4">fatcat:z4zndvivfbafndce2xasxyg6s4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930155001/https://arxiv.org/pdf/2009.03376v1.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.03376v1" 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>

Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning) [article]

Rui Shu, Tianpei Xia, Huy Tu, Laurie Williams, Tim Menzies
<span title="2022-05-02">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms.  ...  Method: We propose an adaptive framework called Dapper, which optimizes 1) semi-supervised learning algorithms to assign pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine learning  ...  Evaluation Metrics If we let TP, TN, FP, FN to denote true positives, true negatives, false positives, and false negatives (respectively), we note that recall (pd), false positive rate (pf), g-measure  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.00665v1">arXiv:2205.00665v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ss6bh2t5mbeajpzietivchugeu">fatcat:ss6bh2t5mbeajpzietivchugeu</a> </span>
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PP-042 Comparison of three methods for the detection of biofilm forming microorganisms isolated from a tertiary care hospital in Pakistan

A. Hassan, J. Usman, F. Kaleem
<span title="">2010</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6v6xxnaax5ay7dkx6nblo7l5l4" style="color: black;">International Journal of Infectious Diseases</a> </i> &nbsp;
This data set was divided into five subsets, four of which for training and the left one for test. In addition, 3000 unlabeled data were used in the semi-supervised learning.  ...  Poster Presentations S37 Result: Out of 267 samples, 133 gave positive results for B. abortus by real-time PCR.  ...  Under antituberculosis agents treatment GST activity extended to rise, but GSH level increased to positive control rate.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1201-9712(10)60110-5">doi:10.1016/s1201-9712(10)60110-5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kv53hg5imnb7hov3ar4gegtdj4">fatcat:kv53hg5imnb7hov3ar4gegtdj4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190307041427/https://core.ac.uk/download/pdf/82371453.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/2d/a2/2da271e252a628562530f5ca9afa42c3c8138bf9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1201-9712(10)60110-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Pattern Based Network Security Using Semi-supervised Learning

Vinod K Pachghare, Vaibhav K Khatavkar, Parag A Kulkarni
<span title="2012-07-25">2012</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4opirqn5wva43mypnaeerrydyy" style="color: black;">International Journal of Information and Network Security (IJINS)</a> </i> &nbsp;
An emerging field of semisupervised learning offers a promising direction for further research.  ...  The supervised learning method exhibits good classification accuracy for known attacks. But it requires large amount of training data.  ...  From figure 3 , we observe that the false positive rate of our proposed algorithm is better than the other approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijins.v1i3.704">doi:10.11591/ijins.v1i3.704</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vfl3kxm2dzbjjdbjwucgpy5hmq">fatcat:vfl3kxm2dzbjjdbjwucgpy5hmq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429083750/http://www.iaescore.com/journals/index.php/IJINS/article/download/16155/9788" 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/b9/08/b9089d17469b6a2f0aafb15b006020dc1be9cf48.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijins.v1i3.704"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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