SVM Meaning and
Definition
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SVM, acronym for Support Vector Machine, is a machine learning algorithm that belongs to the family of supervised learning techniques. SVM is primarily used for classification and regression analysis problems. It is a powerful and versatile method that can efficiently handle both linear and non-linear patterns in data.
In SVM, the algorithm builds a model based on labeled training data. It maps the input data into a high-dimensional feature space using a kernel function and then separates the data points by defining a hyperplane or set of hyperplanes. The objective of SVM is to find the optimal hyperplane that maximally separates the classes while minimizing the margin of error. This hyperplane represents the decision boundary, separating different classes of data points.
The term "support vector" refers to the data points in the training set that lie closest to the hyperplane. These support vectors are crucial in determining the best separating hyperplane. SVMs are defined by a set of support vectors and their associated weights.
One of the key advantages of SVM is its ability to handle high-dimensional and complex datasets efficiently. It is also known for its effectiveness in handling small sample sizes. SVM can be employed for various tasks such as image classification, text classification, sentiment analysis, and bioinformatics.
Overall, SVM is a machine learning algorithm that creates a decision boundary by maximizing the separation between classes, using support vectors and a kernel function.
Common Misspellings for SVM
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