Results 71 to 80 of about 9,444,467 (301)

Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions

open access: yesMathematics
The kernel method is a tool that converts data to a kernel space where operation can be performed. When converted to a high-dimensional feature space by using kernel functions, the data samples are more likely to be linearly separable.
Ke-Lin Du   +4 more
doaj   +1 more source

AGE ESTIMATION WITH REGARD FOR CLASSIFIABLE ABILITY OF EACH COMPONENT IN REDUCED DIMENSION AGE MANIFOLD [PDF]

open access: yesICTACT Journal on Image and Video Processing, 2018
A new age estimation method that takes classifiable ability of each component in age manifold into account is considered. First, we analysis the age classification rate of each component in reduced dimension age manifold.
Pak Duho, Ri Kumhyok, Hyon Cungyong
doaj   +1 more source

Minimal Support Vector Machine

open access: yesCoRR, 2018
Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function uses L2 norm or L1 norm on slack variables. The number of support vectors is a measure of generalization errors. In
Shuai Zheng 0002, Chris Ding
openaire   +3 more sources

Tutorial on Support Vector Machines [PDF]

open access: yes, 2022
Abstract The aim of this tutorial is to help students grasp the theory and applicability of support vector machines (SVMs). The contribution is an intuitive style tutorial that helped students gain insights into SVM from a unique perspective. An internet search will reveal many videos and articles on SVM, but free peer-reviewed tutorials are ...
openaire   +2 more sources

A role for eIF5A in translational control of stress‐inducible proteins in multiple myeloma cell lines

open access: yesMolecular Oncology, EarlyView.
Loss of eIF5A rewires thapsigargin‐mediated endoplasmic reticulum stress responses in multiple myeloma cells by selective translation of mRNA transcripts, consequently dampening thapsigargin‐induced unfolded protein response (UPR) activation and shifting the balance from induction of apoptosis toward cell survival and proliferation.
Demi Both   +7 more
wiley   +1 more source

Unique circulating miRNAome signatures captured with distinct molecular states in Lynch syndrome and sporadic colorectal cancer

open access: yesMolecular Oncology, EarlyView.
Circulating microRNA profiles distinguish nonmalignant Lynch syndrome from cancer‐associated Lynch syndrome, sporadic colorectal cancer, and healthy controls. Age‐stratified analysis reveals disease‐specific regulatory states, and a six‐miRNA panel shows concordant separation in an independent colorectal tissue cohort.
Ramadhani Salum Chambuso   +5 more
wiley   +1 more source

The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection

open access: yesMolecular Oncology, EarlyView.
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński   +12 more
wiley   +1 more source

Versatile vector tools for efficient protein screening across multiple expression systems

open access: yesFEBS Open Bio, EarlyView.
A unified vector toolkit enables rapid protein expression screening across E. coli, insect, and mammalian cells. A single primer pair amplifies the target gene, which is inserted into any vector via a standardized interface. This streamlined workflow eliminates repeated cloning steps, accelerating the identification of optimal expression conditions for
Zhimin Zhu   +5 more
wiley   +1 more source

LINEX Support Vector Machine for Large-Scale Classification

open access: yesIEEE Access, 2019
Traditional soft margin support vector machine usually uses hinge loss to build a classifier with the “maximum-margin” principle. However, C-SVM depends on support vectors causing the loss of data information.
Yue Ma   +3 more
doaj   +1 more source

Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells

open access: yesFEBS Open Bio, EarlyView.
Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim   +4 more
wiley   +1 more source

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