Results 51 to 60 of about 1,480,093 (371)
The Stata Journal: Promoting communications on statistics and Stata, 2016 Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines.
Matthias Schonlau, Nick Guenther
openaire +2 more sources
Perbandingan Reduced Support Vector Machine Dan Smooth Support Vector Machine Untuk Klasifikasi Large Data [PDF]
, 2015 Klasifikasi merupakan pengelompokan objek ke dalam dua atau lebih kelompok yang didasarkan pada variabel yang diamati. Support Vector Machine merupakan metode berbasis machine learning yang sangat menjanjikan untuk dikembangkan karena memiliki ...
Purnami, S. W. (Santi) +1 more
core
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications [PDF]
, 2011 Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints.
A. Chatterjee +23 more
core +1 more source
Molecular Oncology, EarlyView.There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes +20 more
wiley +1 more source
Support Vector Machines with Applications
Statistical Science, 2006 Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques.
Moguerza, Javier M., Muñoz, Alberto
openaire +5 more sources
Molecular Oncology, EarlyView.Dual targeting of AKT and mTOR using MK2206 and RAD001 reduces tumor burden in an intracardiac colon cancer circulating tumor cell xenotransplantation model. Analysis of AKT isoform‐specific knockdowns in CTC‐MCC‐41 reveals differentially regulated proteins and phospho‐proteins by liquid chromatography coupled mass spectrometry. Circulating tumor cells
Daniel J. Smit +19 more
wiley +1 more source
A novel deformation forecasting method utilizing comprehensive observation data
Advances in Mechanical Engineering, 2018 Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study.
Sunwen Du, Yao Li
doaj +1 more source
A fault diagnosis method of rolling bearing
Gong-kuang zidonghua, 2014 For parameter optimization of support vector machine in fault diagnosis method of rolling bearing based on support vector machine, an improved fruit fly optimization algorithm was proposed which took accuracy rate of pattern classification as taste ...
DONG Jianping, YANG Cheng, LU Xiaoli
doaj +1 more source
Hyper-parameter Tuning for Quantum Support Vector Machine
Advances in Electrical and Computer Engineering, 2022 In recent years, the positive effect of quantum techniques on machine learning methods have been studied. Especially in training big data, quantum computing is beneficial in terms of speed.
DEMIRTAS, F., TANYILDIZI, E.
doaj +1 more source
Support Vector Machine-Based EMG Signal Classification Techniques: A Review
Applied Sciences, 2019 This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM).
D. C. Toledo-Pérez +3 more
semanticscholar +1 more source