It takes the average reader 8 hours and 55 minutes to read Regularization, Optimization, Kernels, and Support Vector Machines by Johan A. K. Suykens
Assuming a reading speed of 250 words per minute. Learn more
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.
Regularization, Optimization, Kernels, and Support Vector Machines by Johan A. K. Suykens is 525 pages long, and a total of 133,875 words.
This makes it 177% the length of the average book. It also has 164% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 12 hours and 11 minutes to read Regularization, Optimization, Kernels, and Support Vector Machines aloud.
Regularization, Optimization, Kernels, and Support Vector Machines is suitable for students ages 12 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.
When deciding what to show young students always use your best judgement and consult a professional.
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