What is the Kernel Approach in Non-Parametrised Unbinned Analysis?

In summary, the kernel approach is a versatile method used in various fields such as computing and medicine. It does not specifically address unbinned analysis, but it can be a helpful resource for understanding the fundamentals of the approach. More information can be found in the first principles of kernel computing, as well as in the provided links.
  • #1
lavster
217
0
i came across the term kernel approach when reading about a non parametrised unbinned method of analysis. what does this mean?

cheers
 
Physics news on Phys.org
  • #2
Kernel Approach has many applications, from computing and medicine and beyond. The basics are the same however, and this is not a bad place to learn about the approach, even though it doesn't address unbinned analysis.

First Principles of Kernel Computing: http://en.wikipedia.org/wiki/Kernel_(computing )

This is useful, in that the abstract describes the benefits of this approach: http://userweb.cs.utexas.edu/users/inderjit/public_papers/kernel_icml.pdf

And directly to your question see starting on page 62: http://www.slac.stanford.edu/econf/C030908/papers/MOCT003.pdfFor something a bit unrelated, but possibly illuminating:
http://portal.acm.org/citation.cfm?id=944810&dl=GUIDE&coll=GUIDE&CFID=96211260&CFTOKEN=34267705
 
Last edited by a moderator:

Related to What is the Kernel Approach in Non-Parametrised Unbinned Analysis?

What is a kernel approach?

A kernel approach is a statistical method used in machine learning and data analysis. It involves mapping data into a higher dimensional space where it is easier to classify or analyze using linear methods.

What are the benefits of using a kernel approach?

There are several benefits to using a kernel approach, including the ability to handle non-linear data, improved accuracy in classification tasks, and the ability to work with high-dimensional data without the curse of dimensionality.

What types of data can be analyzed using a kernel approach?

A kernel approach can be applied to various types of data, including numerical, categorical, and textual data. It is particularly useful for non-linear data that cannot be easily separated using traditional linear methods.

What are some common kernel functions used in a kernel approach?

Some common kernel functions include linear, polynomial, Gaussian, and sigmoid kernels. These functions are used to transform the data into a higher dimensional space, where it can be more easily analyzed or classified.

Are there any limitations to using a kernel approach?

While a kernel approach can be very effective in many cases, it does have some limitations. One limitation is the choice of the kernel function, which can greatly impact the results. Additionally, the performance of a kernel approach may degrade for very large datasets.

Similar threads

  • High Energy, Nuclear, Particle Physics
Replies
6
Views
2K
  • High Energy, Nuclear, Particle Physics
Replies
2
Views
1K
Replies
1
Views
450
Replies
2
Views
479
  • Programming and Computer Science
Replies
1
Views
1K
  • Science and Math Textbooks
Replies
3
Views
883
  • High Energy, Nuclear, Particle Physics
Replies
2
Views
3K
Replies
20
Views
2K
  • High Energy, Nuclear, Particle Physics
Replies
8
Views
1K
Replies
2
Views
552
Back
Top