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| Name | Lecture 15 - Kernel Methods |
| Video Uploader | Video From caltech |
| Upload Date | This Video Uploaded At 24-05-2012 16:01:33 |
| Video Discription | Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins. Lecture 15 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 22, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA. |
| Category | Science & Technology |
| Tags | Machine Learning (Field Of Study) | kernel methods | Support Vector Machine | Caltech | MOOC | data | computer | science | course | Data Mining (Technology Class) | Big Data | Data Science | learning from data | kernel trick | SVM | RBF | soft margin | quadratic programming | polynomial kernel | Vapnik | Technology (Professional Field) | Computer Science (Industry) | Learning (Quotation Subject) | Lecture (Type Of Public Presentation) | California Institute Of Technology (Organization) | Abu-Mostafa | Yaser |
