In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of
Learning with kernels : support vector machines, regularization, optimization, and beyond
β Scribed by Bernhard SchoΜlkopf; Alexander J Smola
- Publisher
- MIT Press
- Year
- 2002
- Tongue
- English
- Leaves
- 640
- Series
- Adaptive computation and machine learning
- Category
- Library
No coin nor oath required. For personal study only.
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In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks.
<p><b>A comprehensive introduction to Support Vector Machines and related kernel methods.</b></p><p>In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegan
<p><b>A comprehensive introduction to Support Vector Machines and related kernel methods.</b></p><p>In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegan
A comprehensive introduction to Support Vector Machines and related kernel methods.