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Regularization, Optimization, Kernels, and Support Vector Machines (Hardcover)
Loot Price: R3,216
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Regularization, Optimization, Kernels, and Support Vector Machines (Hardcover)
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Expected to ship within 12 - 17 working days
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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.
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