Learning with Support Vector Machines

By Colin Campbell, Yiming Ying. Learning with Support Vector Machines, 2011

310 citations recorded by the source corpus.

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e044a4b5be1563fccc46e8f7552935b99365f90a

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In Artificial Intelligence

Citations in the corpus

An integrative approach to predicting the functional effects of non-coding and coding sequence variation (2015, methodology): “An SVM for binary classification has a decision function of the form sign ð / Þ with the sign of / determining the predicted class.”
A review on machine learning approaches and trends in drug discovery (2021, background): “Kernels code and measure the similarity between objects [128–130].”
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features (2017, background): “where φa(s) is the count of the number of times sequence a occurs in sequence s, from which a kernel matrix can be readily derived (Campbell and Ying, 2011).”