Results 91 to 100 of about 1,725,137 (297)
Partial least squares (PLS) regression and its application to coal analysis</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>Revista Técnica de la Facultad de Ingeniería</i>, 2003 </span><br><span class="r_content">Los métodos instrumentales de análisis químico hacen uso de las relaciones entre la señal obtenida y una propiedad del sistema estudiado (generalmente, una concentración).</span><br><span class="r_sub"><i>Carlos E Alciaturi<span id="ma_1" style="display:none">, Marcos E Escobar, Carlos De La Cruz, Carlos Rincón</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_1')">+3 more</a></small></i></span><br><small><a href="https://doaj.org/article/1d77c19e48234290ae3611ac64584af6" target="_blank" rel="nofollow" title="doaj.org/article/1d77c19e48234290ae3611ac64584af6">doaj</a> </small> <br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.13374/j.issn2095-9389.2017.01.005" target="_blank" rel="nofollow">Multi-class fault diagnosis of BF based on global optimization LS-SVM</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>工程科学学报</i>, 2017 </span><br><span class="r_content">Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems, a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem.</span><br><span class="r_sub"><i>ZHANG Hai-gang, ZHANG Sen, YIN Yi-xin</i></span><br><small><a href="https://doaj.org/article/7e443b8ead8943af9d3824554c7b8b27" target="_blank" rel="nofollow" title="doaj.org/article/7e443b8ead8943af9d3824554c7b8b27">doaj</a> </small> <div id="more_2" style="display:none"><a href="/sci_redir.php?doi=10.13374%2Fj.issn2095-9389.2017.01.005" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.13374/j.issn2095-9389.2017.01.005'); alert('Copied the doi');">copy doi</a> <small>(10.13374/j.issn2095-9389.2017.01.005)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_2')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1002/2211-5463.70217" target="_blank" rel="nofollow">Establishing an assay to evaluate d‐amino acid oxidase enzyme kinetics and inhibition using WST‐8 redox dye</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>FEBS Open Bio, EarlyView.</i></span><br><span class="r_content">This study investigated a novel WST‐8‐based assay for evaluating d‐Amino acid oxidase (DAO) inhibitors. We confirmed its effectiveness using known inhibitors and found that uremic toxins possess relatively weak inhibitory activity compared to existing drugs.</span><br><span class="r_sub"><i>Kahoko Miyake<span id="ma_3" style="display:none">, Yuki Enoki, Yuka Nakazawa, Kazuaki Taguchi, Kazuaki Matsumoto</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_3')">+4 more</a></small></i></span><br><small><a href="https://onlinelibrary.wiley.com/doi/10.1002/2211-5463.70217?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default" target="_blank" rel="nofollow" title="wiley.com/doi/10.1002/2211-5463.70217?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default">wiley</a> </small> <div id="more_3" style="display:none"><a href="/sci_redir.php?doi=10.1002%2F2211-5463.70217" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1002/2211-5463.70217'); alert('Copied the doi');">copy doi</a> <small>(10.1002/2211-5463.70217)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_3')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1016/j.heliyon.2024.e35045" target="_blank" rel="nofollow">Development of partial least squares regression with discriminant analysis for software bug prediction</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>Heliyon</i></span><br><span class="r_content">Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both.</span><br><span class="r_sub"><i>Róbert Rajkó<span id="ma_4" style="display:none">, István Siket, Péter Hegedűs, Rudolf Ferenc</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_4')">+3 more</a></small></i></span><br><small><a href="https://doaj.org/article/bb379b3562524a97b64760f0cc452707" target="_blank" rel="nofollow" title="doaj.org/article/bb379b3562524a97b64760f0cc452707">doaj</a> </small> <div id="more_4" style="display:none"><a href="/sci_redir.php?doi=10.1016%2Fj.heliyon.2024.e35045" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1016/j.heliyon.2024.e35045'); alert('Copied the doi');">copy doi</a> <small>(10.1016/j.heliyon.2024.e35045)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_4')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1016/j.chaos.2014.03.005" target="_blank" rel="nofollow">Least Squares Shadowing Sensitivity Analysis of a Modified
Kuramoto-Sivashinsky Equation</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16">, 2014 </span><br><span class="r_content">Computational methods for sensitivity analysis are invaluable tools for
scientists and engineers investigating a wide range of physical phenomena.
However, many of these methods fail when applied to chaotic systems, such as
the Kuramoto-Sivashinsky (K-S) </span><br><span class="r_sub"><i>Blonigan, Patrick J., Wang, Qiqi</i></span><br><small><a href="https://core.ac.uk/works/17139913" target="_blank" rel="nofollow" title="core.ac.uk/works/17139913">core</a> </small> <div id="more_5" style="display:none"><a href="/sci_redir.php?doi=10.1016%2Fj.chaos.2014.03.005" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1016/j.chaos.2014.03.005'); alert('Copied the doi');">copy doi</a> <small>(10.1016/j.chaos.2014.03.005)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_5')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1002/2211-5463.70231" target="_blank" rel="nofollow">Evolutionarily divergent DUF4465 domains have a common vitamin B12‐binding function</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>FEBS Open Bio, EarlyView.</i></span><br><span class="r_content">We show that DUF4465 family proteins, widespread across bacteria from gut microbiomes, hydrothermal vents, and soil, share a common vitamin B12‐binding function. These augmented β‐jellyroll proteins bind vitamin B12 via extended loops. Our findings establish sequence‐diverse DUF4465 proteins as a widespread class of B12‐binding proteins, highlighting ...</span><br><span class="r_sub"><i>Charlea Clarke<span id="ma_6" style="display:none">, Michal Banasik, Rokas Juodeikis, Martin J. Warren, Richard W. Pickersgill</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_6')">+4 more</a></small></i></span><br><small><a href="https://onlinelibrary.wiley.com/doi/10.1002/2211-5463.70231?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default" target="_blank" rel="nofollow" title="wiley.com/doi/10.1002/2211-5463.70231?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default">wiley</a> </small> <div id="more_6" style="display:none"><a href="/sci_redir.php?doi=10.1002%2F2211-5463.70231" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1002/2211-5463.70231'); alert('Copied the doi');">copy doi</a> <small>(10.1002/2211-5463.70231)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_6')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://sanad.iau.ir/journal/tfss/Article/1183311" target="_blank" rel="nofollow">Fuzzy Logistic Regression Analysis Using the Least Squares Method</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>Transactions on Fuzzy Sets and Systems</i></span><br><span class="r_content">One of the most efficient statistical tools for modeling the relationship between a dependent variable and several independent variables is regression.</span><br><span class="r_sub"><i>Zahra Behdani, Majid Darehmiraki</i></span><br><small><a href="https://doaj.org/article/6a1ad60ac4664996b26b3703d1078e77" target="_blank" rel="nofollow" title="doaj.org/article/6a1ad60ac4664996b26b3703d1078e77">doaj</a> </small> <br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1021/acsomega.2c01285" target="_blank" rel="nofollow">Strategies and Considerations for Least-Squares Analysis of Total Scattering Data.</a> <b><a href="https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9089679&blobtype=pdf" target="_blank" rel="nofollow">[PDF]</a></b> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>ACS Omega</i>, 2022 </span><br><span class="r_sub"><i>Chepkemboi C<span id="ma_8" style="display:none">, Jorgensen K, Sato J, Laurita G.</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_8')">+3 more</a></small></i></span><br><small><a href="https://europepmc.org/article/MED/35572759#free-full-text" target="_blank" rel="nofollow" title="europepmc.org/article/MED/35572759#free-full-text">europepmc</a> </small> <div id="more_8" style="display:none"><a href="/sci_redir.php?doi=10.1021%2Facsomega.2c01285" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1021/acsomega.2c01285'); alert('Copied the doi');">copy doi</a> <small>(10.1021/acsomega.2c01285)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_8')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1007/978-3-319-24465-5_14" target="_blank" rel="nofollow">Implicitly Constrained Semi-Supervised Least Squares Classification</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16">, 2015 </span><br><span class="r_content">We introduce a novel semi-supervised version of the least squares classifier.
This implicitly constrained least squares (ICLS) classifier minimizes the
squared loss on the labeled data among the set of parameters implied by all
possible labelings of the ...</span><br><span class="r_sub"><i>B Widrow<span id="ma_9" style="display:none">, GJ McLachlan, K Nigam, KP Bennett, L Bottou, M Loog, M Loog, M Opper, O Chapelle, R Rifkin, R Tibshirani, RH Byrd, S Raudys, T Hastie, T Poggio, X Zhu, YF Li</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_9')">+16 more</a></small></i></span><br><small><a href="https://core.ac.uk/works/18082413" target="_blank" rel="nofollow" title="core.ac.uk/works/18082413">core</a> </small> <div id="more_9" style="display:none"><a href="/sci_redir.php?doi=10.1007%2F978-3-319-24465-5_14" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1007/978-3-319-24465-5_14'); alert('Copied the doi');">copy doi</a> <small>(10.1007/978-3-319-24465-5_14)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_9')">+1 more source</a></small><br></div><div class="r"><p class="r_title"><a href="https://doi.org/10.1002/acn3.70246" target="_blank" rel="nofollow">Real‐World Investigation of Satralizumab in Patients With Neuromyelitis Optica Spectrum Disease</a> </p><span class="r_subtitle"><img src="/img/openaccess.ico" alt="open access: yes" title="open access: yes" width="16" height="16"><i>Annals of Clinical and Translational Neurology, EarlyView.</i></span><br><span class="r_content">ABSTRACT Objective
Satralizumab, a monoclonal antibody targeting the interleukin‐6 receptor, has demonstrated efficacy in clinical trials for neuromyelitis optica spectrum disorder (NMOSD). However, its real‐world effectiveness and safety compared to conventional immunosuppressive therapies remain uncertain.</span><br><span class="r_sub"><i>Li‐Tsung Lin<span id="ma_10" style="display:none">, Hui‐An Lin, Sheng‐Feng Lin</span> <small><a href="#" style="color:#808080;" onClick="return toggle_div(this, 'ma_10')">+2 more</a></small></i></span><br><small><a href="https://onlinelibrary.wiley.com/doi/10.1002/acn3.70246?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default" target="_blank" rel="nofollow" title="wiley.com/doi/10.1002/acn3.70246?mi=2or9o2m&af=R&AllField=least-squares analysis&ConceptID=15941&content=articlesChapters&target=default">wiley</a> </small> <div id="more_10" style="display:none"><a href="/sci_redir.php?doi=10.1002%2Facn3.70246" target="_blank" rel="nofollow">openaccessbutton.org (pdf)</a><br><a href="javascript:navigator.clipboard.writeText('10.1002/acn3.70246'); alert('Copied the doi');">copy doi</a> <small>(10.1002/acn3.70246)</small><br></div><small><a href="#" onClick="return toggle_div(this, 'more_10')">+1 more source</a></small><br></div><div class="r"><div style="margin-bottom:2px;overflow:hidden"><div style="display: inline-block; float: left; font-size: small; padding-right: 16px; margin-top: -1px; padding-bottom: 1px;"><a href="/q-partial_least_squares/" class="suggestion"onclick="show_loader();"><b>partial least squares</b></a><br/><a href="/q-pls/" class="suggestion"onclick="show_loader();"><b>pls</b></a><br/><a href="/q-coal_analysis/" class="suggestion"onclick="show_loader();"><b>coal analysis</b></a><br/></div><div style="display: inline-block; float: left; font-size: small; padding-right: 16px; margin-top: -1px; padding-bottom: 1px;"><a href="/q-multivariate_regression/" class="suggestion"onclick="show_loader();"><b>multivariate regression</b></a><br/><a href="/q-chemometrics/" class="suggestion"onclick="show_loader();"><b>chemometrics</b></a><br/><a href="/q-least_squares/" class="suggestion"onclick="show_loader();"><b>least squares</b></a><br/></div><div style="display: inline-block; float: left; font-size: small; padding-right: 16px; margin-top: -1px; padding-bottom: 1px;"><a href="/q-medicine/" class="suggestion"onclick="show_loader();"><b>medicine</b></a><br/><a href="/q-principal_component_analysis/" class="suggestion"onclick="show_loader();"><b>principal component analysis</b></a><br/><a href="/q-linear_regression_mixed_models/" class="suggestion"onclick="show_loader();"><b>linear regression mixed models</b></a><br/></div></div></div><div class="pagenav"><a href="/q-least-squares_analysis/p-9/" rel="nofollow"><b>previous</b></a> <a href="/q-least-squares_analysis/p-8/" rel="nofollow">8</a> <a href="/q-least-squares_analysis/p-9/" rel="nofollow">9</a> <b>10</b> <a href="/q-least-squares_analysis/p-11/" rel="nofollow">11</a> <a href="/q-least-squares_analysis/p-12/" rel="nofollow">12</a> <a href="/q-least-squares_analysis/p-11/" id="next" rel="nofollow"><b>next</b></a> </div><br></div>
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