It takes the average reader 1 hour and 44 minutes to read Support Vector Machines and Perceptrons by M.N. Murty
Assuming a reading speed of 250 words per minute. Learn more
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>
Support Vector Machines and Perceptrons by M.N. Murty is 103 pages long, and a total of 26,059 words.
This makes it 35% the length of the average book. It also has 32% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 2 hours and 22 minutes to read Support Vector Machines and Perceptrons aloud.
Support Vector Machines and Perceptrons is suitable for students ages 10 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
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