Examples of using Support vector in English and their translations into Indonesian
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Support vector machine was used as a classifier.
The classifier used is a support vector machine.
Abe S. Support vector machines for pattern classification.
The data point xi that is corresponding λi is called support vector.
Support vector, text, digit, hatching, bitmap and so on.
The illustration below shows the basic idea behind Support Vector Machines.
Support vector machine applications in bioinformatics.
Application of SVMs to regression problems is known as support vector regression(SVR).
In both cases, Support Vector Machines show an excellent performance.
For classification, k-nearest neighbor(KNN) and support vector machine(SVM) are utilized.
A support vector machine(with a Gaussian kernel) is a nonparametric large-margin classifier.
Mainly, it focuses on kernel machines like support vector machines for classification and regression problem.
Support Vector Machines are based on the concept of decision planes that define decision boundaries.
The focus of Shogun is on kernel machines such as support vector machines for regression and classification problems.
Support Vector Machines(SVM) classification is based on the concept of decision planes that define decision boundaries.
Platt J(1999) Fast Training of Support Vector Machines using Sequential Minimal Optimization.
Support Vector Machine(SVM) is a classification algorithm that widely used to predict the customer loyalty.
If you have used machine learning to perform classification,you might have heard about support vector machines(SVM).
We have seen how Support Vector Machines systematically handle perfectly/almost linearly separable data.
After the Statsbot team published the post about time series anomaly detection,many readers asked us to tell them about the support vector machines approach.
The Support Vector Machine(SVM) is one of the most popular machine learning algorithms for classification and regression.
The method used to study the old classification of students withnominal data type is the method of Support Vector Machine(SVM) and will be compared with the Binary Logistic Regression method.
Support vector machines(SVM) are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data.
Two very common examples of this are graphical models, where inference is often a superlinear operation- think about the n2 dependence on the number of states in a Hidden Markov Model andKernelized Support Vector Machines where optimization is typically quadratic or worse.
Support vector machines[edit] support vector machines SVM are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data.
Support vector machines give you a way to pick between many possible classifiers in a way that guarantees a higher chance of correctly labeling your test data.
The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data.
Support Vector Machines(SVM) are learning systems that use a hypothesis space of linear function in high dimensional feature space, trained with learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory.