Knowledge Discovery with Support Vector Machines
By L. Hamel. 2009
330 citations recorded by the source corpus.
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/b7d89441fcf28ca1a365af4d739709a7075a5db2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Emilio Carrizosa
- Dolores Romero Morales
- Robert A. A. Campbell
- Wanhe Li
- H. Qin
- Ralf Klinkenberg
- Stephen Coggeshall
- Glenn C. Turner
Citations in the corpus
- Data Classification: Algorithms and Applications (2014, methodology): “An introduction to SVM methods may be found in [30, 46, 75, 76, 85].”
- Supervised classification and mathematical optimization (2013, background): “See also [18] [40] [108] [177] [113] for introductory surveys on SVMs.”
- Imaging a Population Code for Odor Identity in the Drosophila Mushroom Body (2013, methodology): “…of our findings, our data were also run through other classifiers (Venables and Ripley, 1999) such as LDA, support vector machines (SVM), and a perceptron-based neural network (Hamel, 2009; SVM was performed using a freely available package, LIBSVM, http://www.csie.ntu.edu.tw/cjlin/libsvm).”