Results 41 to 50 of about 869,216 (289)

Support Vector Regression-based Multivariate Lesion Symptom Mapping

Frontiers in Psychology, 2015
The voxel-based lesion symptom mapping (VLSM) provides a statistical framework for brain function localization through identifying the lesioned foci responsive for the associated functional impairment of the assessed patients (Bates et al., 2003).
Ze Wang, Ze Wang
doaj   +1 more source

Quantum-assisted support vector regression

Quantum Information Processing
15 pages, 5 ...
Archismita Dalal, Mohsen Bagherimehrab, Barry C. Sanders   +2 more
openaire   +2 more sources

A tutorial on support vector regression [PDF]

Statistics and Computing, 2004
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets.
Smola, Alexander, Schoelkopf, Bernhard
openaire   +3 more sources

Penggunaan Support Vector Regression (Svr) pada Prediksi Return Saham Syariah Bei [PDF]

, 2013
Pada artikel ini algoritma support vector regression (SVR) digunakan untukmendapatkan model prediksi return saham syariah di bursa efek Indonsia. Sampeladalah emiten saham dengan likuiditas tinggi selama periode 2012. Pada penelitian iniPembentukan model
Maharesi, R. (Retno)
core  

Plecstatin inhibits hepatocellular carcinoma tumorigenesis and invasion through cytolinker plectin

Molecular Oncology, EarlyView.
The ruthenium‐based metallodrug plecstatin exerts its anticancer effect in hepatocellular carcinoma (HCC) primarily through selective targeting of plectin. By disrupting plectin‐mediated cytoskeletal organization, plecstatin inhibits anchorage‐dependent growth, cell polarization, and tumor cell dissemination.
Zuzana Outla, Jan Kosla, Magdalena Prechova, Lukas Frick, Patricia Bortel, Yasmin Borutzki, Andrea Bileck, Christopher Gerner, Samuel M. Meier‐Menches, Selma Osmanagic‐Myers, Martin Gregor   +10 more
wiley   +1 more source

Prediction of Compressive Strength Using Support Vector Regression

Mendel, 2019
At the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for ...
Goutham J Sai, Vijay Pal Singh
doaj   +1 more source

Supplier selection with support vector regression and twin support vector regression [PDF]

, 2016
Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir.
İmamoğlu, Salih Zeki, İnce, Hüseyin
core  

Recurrent cancer‐associated ERBB4 mutations are transforming and confer resistance to targeted therapies

Molecular Oncology, EarlyView.
We show that the majority of the 18 analyzed recurrent cancer‐associated ERBB4 mutations are transforming. The most potent mutations are activating, co‐operate with other ERBB receptors, and are sensitive to pan‐ERBB inhibitors. Activating ERBB4 mutations also promote therapy resistance in EGFR‐mutant lung cancer.
Veera K. Ojala, Sini Ahonen, Sara Peltola, Aura Tuohisto‐Kokko, Olaya Esparta, Peppi Suominen, Anne Jokilammi, Iman Farahani, Deepankar Chakroborty, Nikol Dibus, Steffen Boettcher, Tomi T. Airenne, Mark S. Johnson, Lisa D. Eli, Klaus Elenius, Kari J. Kurppa   +15 more
wiley   +1 more source

Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity

Molecular Oncology, EarlyView.
Peroxidasin (PXDN) is secreted by melanoma cells and binds the NK cell receptor NKG2D, thereby suppressing NK cell activation and cytotoxicity. PXDN depletion restores NKG2D signaling and enables effective NK cell–mediated melanoma killing. These findings identify PXDN as a previously unrecognized immune evasion factor and a potential target to improve
Hsu‐Min Sung, David Bickel, Lena C. M. Krause, Daria Ezeriņa, Christian Ickes, Julian Wojtachnia, Christine S. Gibhardt, Magdalena Shumanska, Khadija Wahni, Andrea Paluschkiwitz, Julia Malo Pueyo, Ekaterina Baranova, Wim Vranken, Hedwig Stanisz, Ioana Stejerean‐Todoran, Michael P. Schön, Joris Messens, Ivan Bogeski   +17 more
wiley   +1 more source

Stream Flow Forecasting Using Least Square Support Vector Regression [PDF]

Journal of Soft Computing in Civil Engineering, 2018
Accurate forecasting of streamflow for different lead-times is useful in the design of almost all hydraulic structures. The Support Vector Machines (SVMs) use a hypothetical space of linear functions in a kernel-induced higher dimensional feature space ...
Shreenivas Londhe, Seema Gavraskar
doaj   +1 more source