Large-scale Kernel MachinesLéon Bottou MIT Press, 2007 - 396 pages Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically. Contributors |
Contents
Ch 1 Support Vector Machine Solvers | 1 |
Ch 2 Training a Support Vector Machine in the Primal | 29 |
Ch 3 Fast Kernel Learning with Sparse Inverted Index | 51 |
Ch 4 LargeScale Learning with String Kernels | 73 |
Ch 5 LargeScale Parallel SVM Implementation | 105 |
Ch 6 A Distributed Sequential Solver for LargeScale SVMs | 139 |
Ch 7 Newton Methods for Fast Semisupervised Linear SVMs | 155 |
Ch 8 The Improved Fast Gauss Transform with Applications to Machine Learning | 175 |
Ch 10 Brisk Kernel Independent Component Analysis | 225 |
Ch 11 Building SVMs with Reduced Classifier Complexity | 251 |
Ch 12 Trading Convexity for Scalability | 275 |
Ch 13 Training Invariant SVMs Using Selective Sampling | 301 |
Ch 14 Scaling Learning Algorithms toward AI | 321 |
References | 361 |
Contributors | 389 |
| 393 | |