Examples of using Support vector in English and their translations into Spanish
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The classification of individual regions is obtained using a Support Vector Machine(SVM) classifier.
He has strong experience of using support vector machine mathematics to enhance efficiency in both the finance and the not-for-profit sectors.
Support vector machines and other, much simpler methods
deep learning, support vector machines, and naive Bayes.
the most popular are classifiers based on SVM(Support Vector Machines), Naive Bayes
kernel-based methods such as support vector machines have shown superior performance in supervised learning.
Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR.
Synergic Partners will sponsor the Second Data Analysis with R Contest for which participants will have to implement the Support Vector Machines algorithm with the help of MapReduce.
commonly used with Support Vector Machines to repeatedly construct a model
mGene also use machine learning techniques like support vector machines for successful gene prediction.
The modelling techniques studied include artificial neural networks and support vector machines.
pattern recognition systems such as neural networks or support vector machines.
A support vector machine can be used for supervised machine learning,
kernel methods such as the support vector machine(SVM), Gaussian mixture model,
as well as artificial neural networks or support vector machines for supervised machine learning class prediction, classification.
The classification process is based on a hybrid process that combines classification algorithms such as Support Vector Machines, which use example texts
The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way.
this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.
K-means, self-organizing map) Risk Minimization(Support vector regression, support vector machine, linear discriminant analysis)
The support vectors, weights and biases define the trained SVM model.