Results 101 to 110 of about 9,169,616 (322)
Support Vector Machines in R [PDF]
Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language.
Kurt Hornik +2 more
core
RSVM: Reduced Support Vector Machines [PDF]
, 2001 An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation.
Olvi L. Mangasarian +3 more
core +1 more source
Advanced Science, EarlyView.A novel exercise‐inducible myokine acidic ribosomal protein P2 (RPLP2), initially identified from human trials, is presented here, whose circulating levels negatively correlate with clinical anxiety severity. Muscle‐derived RPLP2 enhances hippocampal ribosomal assembly and adult neurogenesis to rescue stress‐induced anxiety deficits.
Peiyu Luo +18 more
wiley +1 more source
SVM-Maj: a majorization approach to linear support vector machines with different hinge errors [PDF]
Support vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual.
Groenen, P.J.F. +2 more
core
Virtual screening of potential bioactive substances using the support vector machine approach [PDF]
, 2006 Die vorliegende Dissertation stellt eine kumulative Arbeit dar, die in insgesamt acht wissenschaftlichen Publikationen (fünf publiziert, zwei eingerichtet und eine in Vorbereitung) dargelegt ist.
Byvatov, Evgeny
core
Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency
Advanced Science, EarlyView.This work integrates multiscale simulations with data‐driven approaches to predict Coulombic efficiency (CE). Multiscale simulations of battery systems are performed to extract Physics‐Guided descriptors and construct a dataset. Machine learning models trained on this dataset are then subjected to interpretable analysis to identify the most influential
Qintao Sun +9 more
wiley +1 more source
Advanced Science, EarlyView.To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong +11 more
wiley +1 more source
Inf.Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease ...
R. Guido +3 more
semanticscholar +1 more source
Advanced Science, EarlyView.Maximizing Nanoscale Disorder in Block Copolymers for Orientation‐Independent SERS Platform toward Non‐Invasive Diagnostics is a nature‐inspired strategy that engineers controlled randomness within block copolymer lamellae to achieve optical isotropy without compromising nanoscale periodicity.
Jin Man Kim +6 more
wiley +1 more source
METODE SUPPORT VECTOR MACHINE DAN FORWARD SELECTION PREDIKSI PEMBAYARAN PEMBELIAN BAHAN BAKU KOPRA
Ilkom Jurnal Ilmiah, 2017 Telah banyak peneliti-peneliti termotivasi dalam meningkatkan kinerja performa prediksi. Support Vector Machine (SVM) metode yang berlandaskan pada teori pembelajaran statistic dan memberi hasil yang menjanjikan akan lebih baik dibanding metode lain. SVM
Ivo Colanus Rally Drajana
doaj +1 more source