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Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with...

This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) - more than...

This book constitutes the thoroughly refereed post-proceedings of the 17th International Workshop on Languages and Compilers for High Performance Computing, LCPC 2004, held in West Lafayette, IN, USA in September 2004.

The 33 revised full papers presented were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on compiler infrastructures; predicting and reducing memory access; locality, tiling, and partitioning; tools and techniques for parallelism and locality; Java for high-performance computing; high-level languages and...