<span>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.
Learning with Support Vector Machines
✍ Scribed by Colin Campbell, Ying Yiming
- Publisher
- Morgan & Claypool Publishers
- Year
- 2011
- Tongue
- English
- Leaves
- 96
- Series
- Synthesis Lectures on Artificial Intelligence and Machine Learning
- Category
- Library
No coin nor oath required. For personal study only.
✦ Subjects
Информатика и вычислительная техника;Искусственный интеллект;Распознавание образов;
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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
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
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