In recent years, many approximate methods have been developed for analyzing queueing models of complex computer systems. These ad hoc methods usually focus on specific aspects of …
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate …
This work develops in depth the idea that recurrent rules can produce rich and complicated behaviours. Distinguishing "agents" from their interactions, it argues that it is the …
Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision. Sparse …
Basic Category Theory for Computer Scientists provides a straightforward presentation of the basic constructions and terminology of category theory, including limits, functors, …
An overview of the theory and application of kernel classification methods.Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. …
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.Today, the volume, velocity, and variety of data …
Provides the basic mathematical techniques necessary for those who are beginning a study of semantics and logics of programming languages. Although the treatment is elementary, …
