Regularization Optimization Kernels and Support Vector Machines

<p><strong>Regularization Optimization Kernels and Support Vector Machines</strong> 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: </p><p></p><ul> <p/><li>Covers the relationship between support vector machines (SVMs) and the Lasso</li> <p/><li>Discusses multi-layer SVMs</li> <p/><li>Explores nonparametric feature selection basis pursuit methods and robust compressive sensing</li> <p/><li>Describes graph-based regularization methods for single- and multi-task learning</li> <p/><li>Considers regularized methods for dictionary learning and portfolio selection</li> <p/><li>Addresses non-negative matrix factorization</li> <p/><li>Examines low-rank matrix and tensor-based models</li> <p/><li>Presents advanced kernel methods for batch and online machine learning system identification domain adaptation and image processing</li> <p/><li>Tackles large-scale algorithms including conditional gradient methods (non-convex) proximal techniques and stochastic gradient descent</li> </ul><p></p><p><b>Regularization Optimization Kernels and Support Vector Machines</b> is ideal for researchers in machine learning pattern recognition data mining signal processing statistical learning and related areas.</p>