Alert icon
We're changing our privacy policy. This stuff matters.  Learn more  Dismiss

On Sequence Kernels for SVM classification of sets of...

Loading...

Sign in or sign up now!
6,409
Loading...
Alert icon
Sign in or sign up now!
Alert icon

Uploaded by on Oct 8, 2007

Google Tech Talks
April 10, 2007

ABSTRACT

Support Vector Machines (SVMs) have become one of the most popular tools for discriminative classification of static data. However, research in SVM classification of dynamic (continuous) data has gained in interest only recently. In this presentation, I first give an overview of existing sequence kernels for classification of sets of vectors. I then present a new family of sequence kernels that generalizes the Generalized Linear Discriminant Sequence (GLDS) kernel. As opposed to GLDS, the new sequence kernels allow implicit normalized expansions in a high/infinite-dimensional feature space (FS). Moreover, they induce a Mahalanobis distance in the FS which...

Category:

Howto & Style

Tags:

License:

Standard YouTube License

  • likes, 0 dislikes

Link to this comment:

Share to:
see all

All Comments (1)

Sign In or Sign Up now to post a comment!
  • Interesting. I too am working on a project in which one of the methods used is SVM

Loading...

0 / 00Unsaved Playlist Return to active list
    1. Your queue is empty. Add videos to your queue using this button:
      or sign in to load a different list.
    Loading...Loading...Saving...
    • Clear all videos from this list
    • Learn more