Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Supervised learning (Machine learning) --- Learning, Supervised (Machine learning) --- Machine learning

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Support vector machines. --- SVMs (Algorithms) --- Algorithms --- Kernel functions --- Supervised learning (Machine learning)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Statistical physics --- Discrete-time systems. --- Supervised learning (Machine learning) --- Stochastic processes. --- Data processing. --- Mathematical models.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Supervised learning (Machine learning) --- Apprentissage supervisé (intelligence artificielle) --- Machine learning --- Apprentissage automatique --- Artificial intelligence --- Intelligence artificielle --- Apprentissage supervisé (intelligence artificielle)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Support vector machines. --- Kernel functions. --- Functions, Kernel --- Functions of complex variables --- Geometric function theory --- SVMs (Algorithms) --- Algorithms --- Kernel functions --- Supervised learning (Machine learning) --- Support vector machines --- E-books